Many daily choices are based on one's own knowledge. However, when predicting other people's behavior, we need to consider the differences between our knowledge and other people's presumed knowledge. Social agents need a mechanism to use privileged information for their own behavior but exclude it from predictions of others. Using fMRI, we investigated the neural implementation of such social and personal predictions in healthy human volunteers of both sexes by manipulating privileged and shared information. The medial frontal cortex appeared to have an important role in flexibly making decisions using privileged information for oneself or predicting others' behavior. Specifically, we show that ventromedial PFC tracked the state of the world independent of the type of decision , whereas dorsomedial regions adjusted their frame of reference to the use of privileged or shared information. Sampling privileged evidence not available to another person also relied on specific interactions between temporoparietal junction area and frontal pole.SIGNIFICANCE STATEMENT What we know about the minds of others and how we use that information is crucial to understanding social interaction. Mentalizing, or reading the minds of others, is argued to be particularly well developed in the human and crucially affected in some disorders. However, the intractable nature of human interactions makes it very difficult to study these processes. Here, we present a way to objectively quantify the information people have about others and to investigate how their brain deals with this information. This shows that people use similar areas in the brain related to nonsocial decision-making when making decisions in social situations and modify this information processing by the knowledge about others use these to modify their information processing according to the knowledge of others. When making social predictions, a good first step is to ask “what would I do?” However, without considering how others differ in their beliefs and knowledge, such egocentric predictions can be problematic, particularly when not all knowledge is shared. Such mentalizing challenges are often discussed in the context of autism, and are a feature of social interactions in daily life. To make better predictions about others, it is important to know what aspects of one's own world view is privileged  and then ignore that information. This is why we wanted to understand how the brain (1) marks some information as privileged and other as shared and (2) selectively uses only the relevant information when making predictions about the world or other people. By understanding the neural and cognitive mechanisms of constructing one's own and others' beliefs to make different predictions, we can also learn more general truths about how the brain allows us to entertain multiple beliefs interchangeably for social and personal purposes alike.We addressed these issues by using a sequential evidence accumulation paradigm. The neural implementation and the algorithm supporting this type of decision-making have been extensively characterized, both in macaque single-neuron activity  and in hSome have suggested that parts of cingulate cortex very close to the above-mentioned decision-related regions, including the gyral part of the mid-cingulate cortex, have a unique role in processing social information e.g., , whereasIn short, this study leverages the well-known computational paradigm of evidence accumulation to understand a core element of human social cognition. Specifically, we are interested in how people track information that is relevant for their own judgment of the world while concurrently considering what another person knows about that information. Furthermore, we wanted to understand how two people construct predictions and make choices from the information they received and from what they know about another person's information state.A total of 28 participants  were included in this study. As this is a novel paradigm in social decision-making, the study could be considered exploratory and it was not possible to perform a power calculation based on previous effect sizes in this context. All participants were righthanded, reported no history of neurologic or psychiatric disorders, and had normal or corrected-to-normal vision. Participants provided written informed consent before the start of the experiment; they received a compensation of €10 per hour for participation, which lasted 2.5 h for training and scanning. All procedures were approved by the relevant ethics authority (CMO Arnhem-Nijmegen) in accordance with the Declaration of Helsinki.During the task, participants viewed a screen where they witnessed the scene from a first-person point of view. They were facing a virtual other person seated behind a table opposite the participant. In between the participant and the other person, a screen was placed on the table. In the first phase of the trial (sampling phase), parts of the screen consisting of a square one-ninth of the screen in size sequentially opened up, revealing a cloud of red and blue dots. Per trial, 3 , 4 (75%), or 5 (12.5%) parts of the screen opened up. Each open section remained visual for 1.5-2.5 s . The open sections had either a white or light gray background, indicating whether the cloud of dots was available to both the participant and the other (shared sample) or only to the participant and not to the other (occluded sample).Occluded and shared samples were drawn from independent distributions but we made sure that occluded samples were more likely to have strong evidence in either direction. This ensured more trials where the occluded evidence had a significant influence on the participant's response than would be expected if the same sampling distributions were used for shared and occluded samples, since shared samples outnumber occluded ones. This also led to participants making different choices for themselves and the other person in 36.7% of all trials. There was no bias as to how many dots of each color were displayed on each screen; the number of dots on a single screen was never >45 but otherwise covered the whole spectrum of blue/red ratios.After viewing all samples, in the next phase of the trial (decision phase), participants were asked to indicate whether they had seen more red or blue dots in total (Self Decision) and whether the confederate had seen more red or blue dots (Other Decision) by pressing a button corresponding to the position of the word “red” or “blue” on the screen. The mapping between answers and buttons was determined randomly on each trial to avoid motor preparation before the decision screen. After the button press was recorded, the corresponding word on the screen was framed in the respective color.In the final part of the trial (feedback phase), participants received feedback on the choice the other person actually made in that trial by highlighting the word indicating that color on the screen for 500 ms. The behavior of the other person was determined using a softmax function of evidence against probability of choosing the correct answer. Softmax inverse temperature varied across “good” other persons (inverse temperature = 0.3) and “bad” other persons (0.03). Thus, the other person's choice was not 100% predictable encouraging participants to observe and learn about the other's ability level. This inferred ability was probed every 10 trials (judgment phase), when participants were presented with a 10 point scale ranging from “She/He is doing very badly” to “She/He is doing very well” in which the participants used the buttons to indicate how well they believed the participants were doing. The other person's identity was changed every 40 trials; all participants encountered two good and two bad others. The appearance and order of the avatars were randomized across participants.Directly before the scanning session, participants completed an extensive four-part training on a personal computer in a dedicated behavioral testing laboratory to introduce them to the different aspects of the task. During the first part , participants performed the task with only nonoccluded screens and were required to indicate whether they had seen more red or more blue dots ; in contrast to the later experiment, they received feedback on the accuracy of their choice. The second part  closely resembled the first, but feedback was absent, as it would be during the scanning session. In the third training session , occluded screens were introduced and participants were also asked to indicate the choice of the other , who was seen but inactive during the first two parts. During this part, the behavior of the other was perfect so the feedback participants received on the behavior of their confederate served as feedback on the other decision. The last part  had all the aspects of the actual experiment in it, with two different others possessing two different ability levels. In contrast to the later experiment, participants were asked to rate the ability of those others every 5 instead of every 10 trials. The ability levels also differed from those in the final experiment to avoid carryover learning effects.B, Self Decision); subsequently, they indicated what their other should say based on the information available to them .The experimental session directly followed the training. Participants conducted the experiment in a supine position, while we collected fMRI data . The task consisted of 160 trials. Participants played with four different others presented in blocks, amounting in 40 trials per other. Those others possessed two different ability levels. Other identity and ability level assignment were randomized to avoid confounds associated with their appearance. Each trial, participants indicated whether they perceived more red or blue  following a Poisson distribution. We provided a possibility for learning about the other's ability by presenting feedback on its actual behavior for participants after they decided on the other's answer. Every 10 trials (4 times per confederate), participants indicated their opinion about the ability of the other on a 1-10 scale.Participants watched a projection of the experiment screen through a mirror placed on top of the MRI receive coil. For each participant, we adjusted the viewing distance individually, according to appropriate head positioning in the scanner. The complete scanning session took ∼90 min: 1 h experimental session, leaving us 30 min for preparation and high-resolution structural scan (see Data acquisition).MRI data were acquired on a 3T Siemens PrismaFit, using a 32 channel head coil. Functional scans were collected in a single run using a gradient EPI sequence, multiband 4, with TR = 1.5 s, TE = 32 ms, flip angle = 75 degrees, and voxel resolution of 2 × 2 × 2 mm. Structural scans were acquired using a T1-weighted MP-RAGE sequence, 1 × 1 × 1.3 mm voxels. All scans provided whole-brain coverage, with EPI slices angled downward in the posterior direction.n is the shared sample numbern is the occluded sample numberTo analyze the choices participants made, we used logistic multiple regressions implemented in MATLAB (glmfit). We ran three separate regression analyses. (1) We analyzed the Self Decisions predicting blue choices and including as regressors the relative evidence for blue dots in the occluded and shared samples separately. As there was more than one sample for each in most trials, we summed all evidence within the samples of one category as follows:Thus, the GLM for the Self Decision was as follows:We ran the same GLM for the Other Decision, except that the data were the other choices as follows:To test whether there is a significant difference between the use of occluded evidence in the Self and Other Decision, we combined all the choices and ran a regression with decision type (self/other) as a main effect and an interaction factor with each type of evidence (shared/occluded) to test for a significant interaction term between self and other and occluded evidence as follows:Furthermore, we ran a repeated-measures ANOVA to test whether the participant noticed the skills of the different avatars. We note that all task regressors were only weakly correlated (<0.3), allowing good estimation of their unique contributions.www.fmrib.ox.ac.uk/fsl). Preprocessing of the functional data consisted of motion correction using MCFLIRT, nonbrain removal using BET, spatial smoothing using a Gaussian kernel of 5 mm FWHM, grand-mean intensity normalization of the 4D dataset by a single multiplicative factor, and high-pass temporal filtering . Functional images were registered to skull-stripped structural images using FLIRT; registration of structural images to MNI152 standard space was conducted using FLIRT followed by refinement using FNIRT nonlinear registration. First-level analyses were performed in each subject's native functional space, group analyses in MNI152 standard space.DICOM images were converted to NIFTI format using MRconvert. Subsequent analyses of MRI data were performed using tools from FSL .In the sampling phase, we had one regressor that compared occluded versus shared trials, the amount of evidence when it was an occluded sample, the amount of evidence when it was a shared sample, the overall sample number for the shared samples in the trial, and the same for the occluded samples.In the Self Decision, we only had the Chosen relative occluded and Shared evidence . Coding evidence and value in a choice frame are common and indicate brain regions that accumulate evidence as such accumulation speed and aggregate activity are modulated by the relative evidence for the option that is ultimately chosen (as this indicates the bound an accumulator ended up integrating toward).For the Other Decision, we had the exact same regressor model, except now coding relative evidence according to the Other Decision and not the Self Decision.Last, we also modeled the feedback period with a binary regressor comparing correct and incorrect other predictions.Z threshold of 2.7 and a cluster p threshold of 0.05. Both positive and negative effects were investigated.First-level results were warped to standard space for mixed-effect group analyses using Feat's FLAME 1 using automatic outlier deweighting. Thresholding was set at a conservative Anatomical labeling was based on the FSL implementation of the connectivity-based parcellation atlases of www.neuroecologylab.org).Group-level results and code implementing the analysis model will be made available online as a Data Sharing Collection on the Donders Repository and linked from the laboratory's website (A), at every sampling event, the participant would be presented a specific number of blue and red dots on a two-sided virtual screen presented on the display. The participant was asked to keep track of the prevalence of red and blue dots to later be able to say whether there were more red or blue dots. Importantly, another person, represented by an avatar, was seated behind the other side of the virtual screen. Participants were told the other person was also trying to estimate blue and red dot numbers. However, while most samples were shared, some samples would only be presented to the participant and thus be occluded for the other person. This manipulation led to a discrepancy between what the participant knew about overall dot numbers and what the other person believed. After every sampling phase, participants were first asked to say whether they believed there were more red or blue dots overall . Afterwards, they had to predict what the other person (avatar) would say regarding the dots . Importantly, as the other person could not know about the occluded samples, those had to be ignored when answering the second question. The trial ended after the participant received feedback on the choice made by the other person.Participants performed a sequential evidence accumulation task with discrete evidence samples. Specifically, over the course of the trial's sampling phase A, at eveWhile every trial provides the participants with multiple samples, throughout the manuscript, we will mostly consider them in aggregate form. We combine all shared samples of a trial into “shared evidence” and all occluded samples into “occluded evidence.” Thus, during the Self Decisions, participants should calculate the evidence based on all samples presented during a trial, combining shared and occluded evidence. In contrast, during the Other Decisions, they should calculate the evidence by taking into account only the shared evidence, ignoring the occluded evidence.D; p values for t tests on both shared and occluded regression weights <0.001, Cohen's d > 2). In contrast, the Other Decision should ideally be driven by dots presented during the shared, but not the occluded, samples. Indeed, choices were strongly influenced by shared evidence . However, those decisions were still biased by occluded evidence  . To formally test whether the influence of the occluded evidence was nonetheless significantly lower on the Other compared with the Self Decisions, we ran a regression combining both self and other choices and included an interaction factor of decision type. This again showed significant effects of shared evidence  and occluded evidence . Decision type had no effect on the participants' choice . However, crucially, there was a large interaction between occluded evidence and decision type , suggesting, as expected, that occluded evidence was used a lot more in Self than Other Decisions.Participants' decisions were analyzed using a logistic multiple regression predicting blue choices based on shared and occluded evidence. This demonstrated that both dots presented during shared and occluded samples significantly affected choices  = 175.018, p < 0.0001, partial η2 = 0.701) and ABILITY × TIME interaction  = 3.646, p = 0.016, partial η2 = 0.119). Post hoc paired-sample t test showed that the difference in rating between good and bad confederates was significant at each time point . Interestingly, although participants were able to detect the different ability levels, this has no effect on their Other Decision, with a regression showing no effect of agent ability nor changes in the use of shared evidence.We wanted participants to think carefully about their predictions of other people's behavior, so every 10 trials we asked them to rate how well they thought the other person was performing the task, based on the feedback they received at the end of every trial. We also made sure there was a difference in performance by blocking the experiment into four phases with a different other person each time as indicated by a changed avatar. Two avatars were “good” and two were “bad” at the task. Participants were able to detect the different levels of performance of the four avatars, rating the good ones consistently higher  than the bad ones (5.46 ± 0.19). A repeated-measures ANOVA on the ratings with factors ABILITY (good/bad) and TIME (each of the four rating moments) indicates that participants assessed the confederates' ability very quickly and improved moderately over time, showing a main effect of ABILITY , but also the MFC  and medial parietal cortex ; Table 1However, what we were most interested in during the sampling period was the difference between both types of sampling events. Thus, we looked at the difference between sampling of occluded compared to shared evidence. This showed increased activation in superior temporal sulcus extending into the temporoparietal junction area (TPJ) , medial Our main goal in this experiment was to investigate how regions of MFC are involved in translating the accumulated evidence into a concrete action, and whether this differs based on whether the action needs to be based on knowledge about the world or on information known to a task partner.A; Fig. 3B; First, we tested for expected effects of evidence strength on deciding about the state of the world  during the Self Decision. We found strong effects of evidence strength in ventromedial PFC  and posterior MFC . BOLD signal intensity decreased as evidence strength increased  in dorsomedial PFC  for both shared Fig. 3A;  and occlThese contrasts demonstrate that our paradigm allows us to differentiate the main effects commonly reported for the various MFC subdivisions during decision-making. However, we were most interested in the activation patterns when participants had to predict what others will do, instead of deciding about the state of the world overall. When deciding about others' view of the world during the Other Decision, the decision had to be based on the shared evidence only, while ignoring the occluded samples.D; Therefore, we next investigated whether the reported regions of MFC dissociate between the Self Decision and the Other Decision, where privileged information presented only to the participant in the occluded evidence should be ignored. We compared the effect of occluded evidence in Self Decisions, when it should be just a straightforward part of the evidence integration process, with occluded evidence in the Other Decisions, where it should be ignored. Doing so revealed a large effect in dmPFC  Fig. 3D; , but no E; From this whole-brain comparison contrast, it is possible to infer that dmPFC is successfully ignoring the occluded information when making decisions, while vmPFC is still including it in its consideration as it is part of what the person knows to be the true state of the world. However, alternatively, dmPFC could be actively suppressing occluded evidence to prevent such knowledge to contaminate one's judgment about the other person. Given that both options are possible when looking at a comparison contrast , we wanted to explicitly test whether occluded evidence might activate dmPFC, despite it being deactivated with more choice evidence when judging the whole state of the world. Thus, we tested for activation with occluded evidence in Other Decisions on the whole-brain level. Here we found activation of the pre-SMA, partially overlapping with the dmPFC cluster reported in the previous analysis Fig. 3E; . dmPFC tt(27) = 5.2127, p < 0.001; occluded t(27) = 4.3887, p < 0.001), and this activation is sustained during the Other Decision (shared t(27) = 3.5557, p = 0.0014; occluded t(27) = 4.3634, p < 0.001) (C). This consistent activation of vmPFC is also emphasized by a conjunction analysis . In contrast, the time courses of pre-SMA differ dramatically between the Self and Other Decisions, with a large negative effect of occluded evidence during the Self Decision (t(27) = −2.1754, p = 0.0385) shifting to a positive effect during the Other Decisions (t(27) = 2.2120, p = 0.0356; Fig. 3F), while the sharp negative signal for the shared evidence during the Self Decision (t(27) = −6.6359, p < 0.001) is somewhat reduced during the Other Decision (t(27) = −2.2289, p = 0.0343. We note that, since the vmPFC ROI time course was selected based on the shared evidence in the Self Decision contrast and the pre-SMA ROI time course on the Other Decision occluded evidence, we report their convolved statistics for completeness sake and use the time courses as illustration as neither is an unbiased test of significance.This dissociation between ventral and dorsal part of the MFC is also evident in the time courses of activation during the decision phase. Both shared and occluded evidence affects activation in vmPFC during the Self Decision (convolved HRF effect shared < 0.001) C. This canalysis  of all eDuring the Other Decision, the participant had to consider that some of the evidence they had was privileged as it was occluded for the other person. Above, we demonstrated that areas around the TPJ were more active when a sample was occluded for another person during the sampling phase. This suggests that TPJ might be engaged with considering information that is socially relevant so that the participant can make a prediction about the other person's behavior later. If this is true, it would be reasonable to expect that TPJ interacts with the relevant prefrontal regions during this sampling and during other decisions. We tested this notion using a psychophysiological interaction  analysest(27) = 2.7884, p = 0.0096). Importantly, FPl is frequently implicated in value-based decision-making and learning  = 3.0849, p = 0.0047), but not during Self Decisions (t(27) = 1.6979, p = 0.1010).During the sampling phase, TPJ indeed had increased connectivity with the FPl when a sample was occluded compared with shared . By objectively quantifying knowledge used during social and personal decision-making, we differentiated the roles of two prefrontal brain regions in making decisions about oneself and predicting other people's behavior based on shared information, but not information about the world privileged to the participant.There are at least two ways a person might solve our social sequential evidence accumulation and decision task. They could simply accumulate two evidences in parallel: one for the shared and occluded evidence combined to make “Self Decisions” and one only for the shared evidence to make predictions about the other person. Alternatively, a person could accumulate all evidence together but keep track of or otherwise tag occluded evidence to be able to construct predictions about other agents when necessary, by subtracting such privileged information from one's own expectation of the world. The advantage of the second approach is the ability to rely on most of one's own knowledge to construct only one world model and simply modify it to predict the behavior of others. This ego-centric perspective, which is modified to take other people's perspectives, has the added advantage that it allows simulation of many different types of others by simply modulating other aspects of one's world view. This would be consistent with the observation that egocentric biases grow as people are more cognitively loaded , suggestDuring the sampling phase, when the participant is simply monitoring and integrating evidence for their own judgment about the world, we found increasing activation in a widespread parietal-frontal network. Such patterns of activation are expected based on these regions' general role in action selection and have been shown during similar task settings . ImportaDuring the Self Decision, we found activations that would be expected in this task without any social manipulation. Both evidence positive and negative brain regions in parietal and lateral and medial PFC activated the way they do in value-based decision-making . HoweverIt has been argued that the well-established mechanisms of reinforcement learning can be applied to social information processing by changing the frame of reference in which the information is processed . This apOur data could help bridge the gap between the personal, egocentric decision-making literature concerned with how people optimize their own rewards and the social literature that is more interested in how we understand other people and predict their behavior. Previously, in social decision-making, the distinct roles of vmPFC and dmPFC have often been interpreted in terms of a dissociation between processing emotional value and attribution of intentional state . This inOverall, our data offer some evidence for the notion that, instead of there being a dedicated social network that in one step simulates and predicts other people's behavior from scratch only using social information, social predictions might be a two-step process. The first step is the retrieval of the relevant egocentric knowledge about the world as you would for personal choices, which some brain regions, such as the vmPFC, hold naturally when making decisions about the self. The second step involves contextualization and modification of this knowledge by one's knowledge of the world view of the other person. Neurally, regions, such as TPJ, might supply information about what the other does not know. dmPFC contextualizes and modifies the prediction by this context, as it would for personal decisions that require contextualization or suppression of irrelevant information. Psychologically, our data suggest that predictions about other people can start from one's own egocentric perspective, which is then altered, at least if one predicts their behavior based on a substantially shared world. This perspective is further supported by participants' inability to completely ignore their privileged knowledge when predicting others.The current paradigm of using evidence accumulation in a social setting allowed us to successfully dissociate signals related to processing knowledge about the world and about others. The paradigm has the potential to be further developed to investigate other signals of relevance to social and communicative information processing, including dealing with a lack of knowledge on the part of the participant and overcoming biases in the information processing of the confederate or explicitly learning about the different confederates' abilities. Moreover, it would be interesting to investigate a setting in which the two participants are working toward a common goal, as would be the case in cooperative social interactions. Finally, although we ran analyses coding evidence in terms of whether it reaffirms or changes the current decision, we found no strong signals relating to these effects. This might be because participants are not making decisions at that stage yet, but instead only trying to track the overall evidence levels to choose later. The precise moment of the decision might also be probed in the future using more time-sensitive measures, including neural perturbation techniques.


One example is Let's Talk about Children (LTC), a brief family-focused intervention that promotes parent, family and child wellbeing. Let's Talk about Children has been developed and adapted to respond to the context into which it has been established, leading to different descriptions reported in its 20 year collection of evidence. Collating the diverse literature on LTC, this paper showcases an evidence-based practice developed in-situ in order to guide future innovation.Traditional models of evidence-based practice assume knowledge is developed in research settings before being installed in practice settings. The role practice settings can play in enhancing effectiveness and enabling sustainability is not therefore acknowledged. Developing interventions Using an integrative review, key literature using LTC were identified through electronic databases and snowballing techniques. Constant comparison analysis synthesized the data to develop patterns and themes.in-situ.From the 26 records, three forms of LTC were identified and outcomes related to parents, family and child wellbeing, implementation and sustainability were collated. Consolidated outcomes show overall agreement in effectiveness and acceptability outcomes across different settings and populations. Implementation and sustainability impacts are entwined with the context, and influenced by its development in-situ model is effective at developing sustainable evidence-based practice. In consolidating the evidence, the review clarified LTC's forms and outcomes, and draws attention to the importance of research on mechanisms of change.The study documents that the  LTCThe purpose of LTC is to promote family mental wellbeing while also mitigating and/or preventing mental health issues for both parents and children . LTC takLTC is described as a “low threshold public health intervention”  because in-situ can pose unique complications. As it is adapted and developed to fit the practice settings and the population, the way it is described in the literature can vary and its focus audience differ. As a consequence, a clear understanding of the evidence-base can be challenging.Drawing together the evidence for a practice developed in-situ in order to guide future innovation. The study collates the diverse literature on LTC, to identify its forms and outcomes, and explore the implementation and sustainability impact of developing evidence-base practice in-situ. This study used the following questions:Using LTC as an example, this paper showcases an evidence-based practice developed What was the context of the study ?How was LTC described?Was LTC studied alone or with other interventions?What outcomes, implementation and sustainability impacts were documented?An integrative review method which permits reviewing qualitative and quantitative literature was used to consolidate what was known about LTC based on the research questions .* Talk about the Children and Let* Talk about Children. Additional peer-reviewed and gray literature was found through “snowballing techniques”  using the search terms of Lethniques”  and direA total of 149 records were identified via the database search with an additional 7 records via snowballing. After duplicates were removed, 89 records were screened at title and abstract removing an additional 31 records. The remaining records' full text were then assessed for documenting outcomes for LTC, resulting in 26 records included in review see .Data was extracted by the primary author and entered into a matrix according to review questions. Constant comparison was used to group extracted data into systematic categories to enable data synthesis through the identification of patterns and themes consistent with integrative review methods .The study identified 26 records documenting outcomes for LTC; five of randomized control trials (RCT), three Quantitative papers, five Qualitative papers, seven of mixed method studies and six descriptive and commentary records see , 2. The The research endeavors in regards to LTC have included a focus on its effectiveness, safety and acceptability in its different forms, in different settings and in different populations –27, 38. The records document LTC as evolving to fit its context. While the controlled adaptations to LTC resulted in changes in the way it was described in the literature, its different forms are recognized as developments of the same intervention as noted in Initially, LTC (LT-1) was described as a conversation with parents about their children, and included the provision of guidebooks –17 and dAfter the data collection for the ECF RCT ended in 2006, LTC was described as a series of structured conversations including an introduction invitation and a set of two discussions which used a structured log and provided parents with guidebooks (LT-D) , 21, 26.Subsequently, LTC was adapted to a Finnish public health intervention delivered to the general population without an underlying risk or problem. The motto being “Every child is worth a discussion” . This incorporated a whole-of-region approach with education settings and services working together as part of the national strategy , 44. NewThe first RCT on LTC, carried out in Finland, was based on LT-1 –22, 37, As the outcomes of LTC's different versions are in agreement, the following documentation does not differentiate by version. LTC has been found to be acceptable for parents , 26, 27 Studies on LTC document improvements in parents' mental health and wellbeing, in their parenting skills and confidence, and in their relationship with their children. Mental health and wellbeing improvements included decreased anxiety and depression –27, 36, Shifts in parenting included improved confidence in parenting and greater self-acceptance , 22, 26 LTC studies document a decrease in negative outcomes and an increase in promotive factors for children. Improvements included a decrease in child depression , anxietyChild outcomes were significantly associated with improvements in family functioning . Child pThe records document different contexts impacting implementation and sustainability of LTC. These include diverse approaches to building workforce capacity, adaptations made or required, and commonalities of organizational capacity to support practice.LTC was developed and implemented to meet a need to develop adult mental health practice that satisfied Finnish minimum standards in welfare and health care legislation , 20, 21.Records note implementation outside Finland as piloting or trialing a cost-effective evidence-based practice and/or testing its applicability to the different cultures or populations , 27. In To enable LTC to fit these cultures and populations, self-help booklets and the log were translated , 38, 45,Different approaches were used for building workforce capacity to deliver LTC. Where implementation was embedded within broader shifts, such as in Finland, building workforce capacity included an initial broad awareness-raising process prior to the method training. Regional campaigns aimed at the public and professionals in health, social services and education sectors, ran open seminars and media coverage. These built awareness on family and child experiences of parental mental disorder, the importance of prevention and promotion in child and family mental health and the basics of preventive interventions . The subWhere implementation was piloting or trialing a cost-effective evidence-based practice, such as Australia and Japan, specific LTC method training is documented as the focus of workforce capacity building. In Australia, this took a variety of approaches; a train the trainer model , 46, onlIn the USA, where implementation was embedded within an adaptation process, a comprehensive change process using a learning collaborative was documented that incorporated in-person training, virtual hubs, coaching and debriefing . While cOverall, the training of LTC was identified as effective for increasing practitioners' skill and knowledge about the impact of mental illness on parents, children and families , 38, 46 Common organizational capacities important for implementing and sustaining LTC are identified in the records. Organizational ownership with multiple levels of leadership and internal implementers is noted as vital for sustainability , 40, 42.in-situ, focusing on parent and family, child and implementation and sustainability outcomes. It demonstrates the relevance of this approach to developing evidence-based interventions.We studied the consolidated evidence of LTC as an example of an evidence-based intervention developed in-situ means that intervention development is influenced by context and knowledge from evolving experience over time (in-situ allows the practice context to influence the shaping of the intervention (Development ver time . This cover time . While arvention .in-situ enabled a rapid response to an acknowledged problem, rather than waiting for a fully-developed intervention with a research base. LTC's initial practice was able to immediately address a known need while continuing to evolve, based on knowledge of the practice setting and the needs and experiences of practitioners and family members.Our study demonstrates how intervention development in-situ brings the work on effectiveness and implementation together. Under these circumstances, it is less likely that an intervention that does not fit the practice setting could be deemed effective and the suitability of an intervention is measured in the light of adjustments made within the organization. The documentation of the spread and sustainability of LTC within Finland (The current review of LTC highlights that developing an intervention to fit the setting while simultaneously developing its evidence-base may also be advantageous for building interventions that can be sustained in real world settings. The alignment between the setting and LTC, vital for sustainability , can be  Finland , 35, 36 in-situ, however, is the ability to consolidate the evidence base and draw together a clear understanding of the practice. As seen in this study, the intervention's description shifted as it was adapted to context, culture and population, and outcomes were published in different fields over many years. The three forms of LTC identified in this review are thus consistent with a practice shaped by the setting, with an evolving body of evidence. Regardless of LTC version, the studies document similar outcomes for children, parents and families. The referenced studies in this review, however, lack detailed descriptions of LTC's adjustments and analysis for subsets of families, limiting clear understanding for who it is and is not a good fit for. This remains a task for future studies. Collectively, the evidence also draws attention to possible core mechanism of change for LTC. Having these clearly articulated could promote adaptations that result in the same expected outcomes and provide guidance for its implementation and evaluation.A challenge for developing evidence-based interventions The example of LTC invites different ways to consider evidence-based practice. Rather than the action of an intervention being defined and manualized, the evidence-base for the core mechanisms of change could be clearly articulated, to enable practices to be fitted to settings. The focus shifts then from fidelity of a manualized intervention, to measuring how the core mechanisms are enacted within practice. As seen for LTC, it is uncommon, however, for these core mechanisms of evidence-based practices to be articulated and have measures identified , or for in-situ through an integrative review of literature on LTC. In consolidating the evidence, the review clarified how the three forms of LTC reported in the literature, document similar outcomes for children, parents and families, and provide a window into its spread and sustainability. The results suggest that intervention development influenced by the practice context provides benefits for implementation and does not compromise the evidence-base. Intervention development in-situ is a relevant developmental pathway for evidence-based practices. Clear articulation of the core mechanisms of change is important for consistent evaluation and reporting the adjustments made in the intervention in different settings will support future in-situ evidence-based practice development.This study set out to showcase an evidence-based practice developed The original contributions presented in the study are included in the article/BA designed and led the data collection and early analysis in consultation with TS. Both authors analyzed the data, contributed and edited the manuscript, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Public health emergencies—such as the 2020 COVID-19 pandemic—accelerate the need for both evidence generation and rapid dissemination and implementation (D&I) of evidence where it is most needed. In this paper, we reflect on how D&I frameworks and methods can be pragmatic  tools for rapid and iterative planning, implementation, evaluation, and dissemination of evidence to address public health emergencies.The PRIDI cycle is based on a “double-loop” learning process that recognizes the need for responsiveness and iterative adaptation of implementation cycle (inner loop) to the moving landscapes, presented by the outer loops of emerging goals and desired outcomes, emerging interventions and D&I strategies, evolving evidence, and emerging characteristics and needs of individuals and contexts. Stakeholders iteratively evaluate these surrounding landscapes of implementation, and reconsider implementation plans and activities.Even when the health system priority is provision of the best care to the individuals in need, and scientists are focused on development of effective diagnostic and therapeutic technologies, planning for D&I is critical. Without a flexible and adaptive process of D&I, which is responsive to emerging evidence generation cycles, and closely connected to the needs and priorities of stakeholders and target users through engagement and feedback, the interventions to mitigate public health emergencies , and other emerging issues, will have limited reach and impact on populations that would most benefit. The PRIDI cycle is intended to provide a pragmatic approach to support planning for D&I throughout the evidence generation and usage processes. Public health emergencies—such as the 2020 COVID-19 pandemic—dramatically accelerate the need for evidence generation and synthesis, as well as the rapid dissemination and implementation (D&I) of evidence-based practices and interventions . In a maD&I science has emerged as an evolving field to address the well-documented gap between research and practice . Dissemihealth or healthcare problem itself may be dynamic and rapidly changing. In February 2020, the main concern of many health systems was implementing case finding and quarantine strategies; in March 2020, it was allocating intensive care unit (ICU) beds and ventilators; in May 2020, safe strategies to gradually lifting lockdowns /PRISM  framework , the intInterventions generally include a core (the essence or function of the intervention that is responsible for its impact) and an adaptable periphery (that could be modified to adapt to various contexts and situations) . IgnorinD&I adaptation models may be useful to help guide planned adaptations  , to helpCOVID-19 is a great example of the importance of implementing solutions as their evidence base is continuously evolving over time. This includes the evidence supporting diagnostic tests and therapeutic interventions, systemic interventions , and social issues and their corresponding interventions (such as approaches to address vaccine hesitancy). Such interventions are being implemented while their evidence base is limited and evolving . Under tThe experience of COVID-19 boosted efforts to revise the traditional evidence pipeline to be more responsive to dynamic changes , and to Evaluation is not a one-time post-intervention process in D&I; it is an iterative, ongoing process that can enhance and inform the evolvability of evidence-based interventions and strategies, including their design, adaptation, refinement, and delivery throughout the process of D&I. Consequently, intended goals and outcomes of D&I should ideally be incorporated from the beginning ; be effective ; be widely adopted (Did the settings and stakeholders/decision-makers adopt the intervention?); be implemented ; and be maintained/sustained (Did the target users continue using it over time and did it continue to have long-term impact?). Importantly, in light of dynamic contexts [RE-AIM provides a systematic conceptual framework to guide the planning, adaptation, and evaluation of the D&I activities, programmes, practices, or policies , 50. An contexts , RE-AIM contexts , 52.Glasgow et al. (2020) applied RE-AIM iteratively in a participatory process to support prospective adjustments during implementation projects . ThroughThe upper side of Fig. feel more invested to help disseminate, implement, and sustain an intervention or public health practice;be prepared cognitively and operationally and be more committed to execute plans for adoption of an intervention or public health practice;identify setting- or cultural-specific barriers that may have been have missed;provide real-time feedback on whether strategies are working and inform important refinements or adaptations of interventions and strategies; andenhance relevance and fit, and may propose innovative solutions.It may seem like an inconvenient time to engage stakeholders in the context of emergency situations. However, even brief engagement of stakeholders has immense benefits that make it worth prioritizing, at the design phase and through the cyclical process of re-evaluation and redesign . StakehoStakeholder engagement may be applied at different degrees along the spectrum of implementation, depending on the availability of time and resources and the nature of the intervention and D&I strategies . Even atAll mentioned processes are only possible under the context of strong organizational commitment , as wellreach, effectiveness, adoption, implementation, maintenance. Recent extensions of this model can also inform more explicit consideration of understanding and addressing health equity and equitable implementation over time and in dynamic contexts [In this paper, we reflected on the cyclical model of Assess > Plan > Do > Evaluate > Report , the RE-contexts . InterveThe cyclical process of D&I informs double-loop learning processes that may result in revisiting mental models, goals and outcomes, interventions and D&I strategies, and individuals and contexts. The results of cyclical evaluations should also be communicated with local implementers and stakeholders through customized and actionable feedback. Stakeholder engagement is a key solution to understand and address contextual variations and barriers. It is a continuum ranging from informing the stakeholders to co-ownership, and will be critical to addressing some of the striking racial/ethnic and setting inequities evidenced for COVID-19, including redistribution of decision-making and resources with the community. Learning from and with communities is broadly recognized as an important source of evidence to guide learning organizations and health systems .During an epidemic, the priority of the health system is provision of evidence-based prevention and treatment, while the priority of the research community is rapid development of effective diagnostic and therapeutic technologies. Even though the health system priority at this moment is the provision of the best care to the individuals in need and the development of effective diagnostic and therapeutic technologies , prospecKey to preparing for national emergencies such as COVID-19 are the development of infrastructures, organizational cultures, trainings, and establishment of processes towards a rapid-learning health system (LHS) , 67 thatThis paper calls for dynamic and adaptive D&I models that are responsive to the rapid and unpredictable nature of emergencies through a double-loop process  involving rapid and iterative cycles of implementation through continuous engagement of stakeholders that are embedded in and adapted for the emergent and evolving nature of goals, interventions, evidence base, and contexts. Establishing these models is essential to preparing for the next national health crisis.


Interpretation of averaged evoked potentials is difficult when the time relationship between stimulus and response is not constant. Later components are more prone to latency jitter, making them insufficiently reliable for routine clinical use even though they could contribute to greater understanding of the functioning of polysynaptic components of the afferent nervous system. This study is aimed at providing a simple but effective method of identifying and quantifying latency jitter in averaged evoked potentials. Autocorrelation techniques were applied within defined time windows on simulated jittered signals embedded within the noise component of recorded evoked potentials and on real examples of somatosensory evoked potentials. We demonstrated that the technique accurately identifies the distribution and maximum levels of jitter of the simulated components and clearly identifies the jitter properties of real evoked potential recording components. This method is designed to complement the conventional analytical methods used in neurophysiological practice to provide valuable additional information about the distribution of latency jitter within an averaged evoked potential. It will be useful for the assessment of the reliability of averaged components and will aid the interpretation of longer-latency, polysynaptic components such as those found in nociceptive evoked potentials. Clinical neurophysiology uses potentials recorded from the human scalp, evoked by peripheral stimulation, to investigate the integrity of the neural pathways of various sensory modalities . The conHowever, the underlying neurophysiology of the production of the evoked potential (EP) is not compatible with the assumptions of averaging. In particular, there is evidence that evoked potential components vary in both amplitude and latency on repeated recordings, with later components displaying greater latency variations (“jitter”) . Signal Early components of scalp-recorded evoked potentials represent activity from fast neural pathways, which only have a few synapses and therefore have the least latency variation. This makes them sufficiently consistent and reliable for clinical use . Howeverr). This technique quantifies the similarity in shape of the evoked potential produced by each external stimulus, within specified time windows. Amplitude variation of discrete components alone does not affect the median r value. An averaged component with a high value of median r indicates that the constituent single responses are occurring at the same latency and have the same shape. Averaged components with low median r values could indicate either interference from other overlapping components unrelated to the stimulus or that the single responses have variation in latency. This study extends the utility of this approach by additionally identifying the distribution of the latency shift exhibited by any specified component. The proposed analysis technique could be performed in parallel with the acquisition of conventionally averaged responses and can give important additional information about the role of latency variability in components with low signal reliability. In addition, information about the nature of latency variation may be relevant for both clinical and research contexts [A previous study by the authors  describecontexts .Other methods have been used to take account of latency jitter of EP components: Achimowicz , for exaSomatosensory evoked potentials (SEPs) were recorded, with informed consent, from two neurologically normal subjects. These were used as genuine biological samples in the testing of the autocorrelation method. SEPs were obtained after surface stimulation of the median nerve at the right wrist, with 0.2 ms electric pulses delivered at the rate of 0.83 Hz and intensity set at the threshold for thumb twitch. Recordings were performed from C3′-Fz scalp derivation with surface electrodes, where C3′ is the usual upper limb SEP recording position, over the hand projection in the sensory homunculus . The samSimulated single evoked potential components were constructed using NI LabView 2017® [δt, then by time shift 2δt, etc., until the time shift was equal to the time window length. This was repeated for all possible pairs of records. This is illustrated in Autocorrelation methods were used for identification of latency jitter. Autocorrelation is a method which can identify similar signals that are delayed in time with respect to each other . For thiN repeated EP records, either real or simulated. An autocorrelation was then performed on every pair of records !), using the LabView function crosscorrelation.vi, and the time difference for the maximum correlation for all pairs of records was plotted as a histogram.A virtual instrument was constructed in LabView 2017 which identified a specified time window within a set of r) within a defined time window has been used as a measure of the reliability of signal components where reliability is defined in terms of repeatability [r, this reliability is therefore expressed as components of similar shape occurring at the same latency. However, there is evidence that evoked potential components vary in both amplitude and latency, with later components displaying greater latency variations (“jitter”) [r), using simulated signal components.Conventional signal averaging ideally requires a stationary signal and random noise in relation to the stimulus onset . The medtability . As chanjitter”) . This ser values for every 10 ms window are shown in red and are a measure of the repeatability of the signals within those windows (see above). The median r values of successive 10 ms windows rapidly decrease with increasing latency variation. With no jitter , indicating that this component is highly reliable. When a small amount of jitter (±5 ms) is added to the simulated component (r has decreased (c. 0.5), which reflects the increased unreliability of this component. With ±10 ms jitter .o jitter , the simomponent , the amps jitter , the aveAn autocorrelation was performed to investigate latency jitter in specified components. A specified time window was defined to fit the component under investigation. The correlation coefficient between two records for this time window was calculated and repeated with one of the records successively time-shifted by a specified amount . The time shift that produced the highest correlation coefficient was recorded, and this was repeated for every possible dissimilar pair of records. The results were displayed as a histogram of the time shifts that produced the maximum correlation for all possible pairs of records. The flow chart for this process is shown in x-axis, which is equivalent to reflecting the left (negative) portion of the distribution curve on to the positive portion. It can be clearly seen that the resulting measured latency shifts approximate to the positive portion of a normal distribution, reflecting the actual jitter distribution. The maximum time difference was 20 ms, which is equivalent to the ±10 ms jitter specified in the simulation.r  goes from 0, where there is only noise, to approximately 0.05 for the width of the averaged signal, indicating poor reliability. However, the histogram from the autocorrelation (r) and autocorrelation, therefore, gives an accurate representation of the underlying signal structure by enabling signal components with low reliability to be differentiated into those with large noise or artefacts  from those with repeatable, but latency shifted, components .relation  clearly r) and autocorrelation (peak difference detection) methods on a real somatosensory evoked potential (SEP) recording. The method of recording the SEP is detailed in Materials and Methods (section 2) Thirty repetitions were conventionally averaged and are shown in r values for these three time windows are superimposed on the averaged graph. The early component has a high median r value (c. 0.8), indicating high reliability in terms of both shape and latency. Figures r value of c. 0.5, indicating worsening reliability, and this is confirmed by the results of the autocorrelation shown in r associated with the later component (C) was low, at 0.13. This is likely to be due to large latency variations, as verified in The time difference histograms shown in Figures This paper describes a simple way of adding latency variation (jitter) information to evoked potential components identified by conventional signal averaging. This is especially important for later components which are polysynaptic in origin and are therefore highly prone to variation in latency. The method described in this paper can quickly identify the distribution of latency variation and estimate the total jitter range.The method used is intuitive and is designed to be used in conjunction with the conventional interpretation of the averaged evoked potential. It gives a clear, graphical indication of the distribution of the latency jitter of the recorded signal within any specified time window. This could be used, in conjunction with measures of component reliability , to idenLatency jitter information would be particularly valuable when using nociceptive evoked potentials. The difficulty of reliably activating nociceptive pathways and the variability of amplitude and latency of the resultant evoked responses have limited the utility of this modality in clinical practice, although there is considerable evidence of their value –23. Howe


Our algorithm employs the softmax function—a generalisation of the logistic function to multiple dimensions—which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA3 has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA3 can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act—approved in 2016 by the US Congress—permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA Real World Evidence (RWE)  is one oDespite being known for a long time and in some cases applied as an informative support in the drug approval process   the softmax function—a generalisation of the logistic function to multiple dimensions—as an instrumental tool for aggregation within (2) a Bayesian decision making framework. While the softmax function was initially introduced in statistical mechanics, it has now found wide-spread applications in machine learning and artificial intelligence methods at large –38. On tEvidence Appraisal Aggregation Algorithm, EA3 (suggested pronunciation: “EA-cube”) compressing a generic vector of evidence appraisals along multiple dimensions into a scalar. Roughly, input data  are first processed through the softmax function and next aggregated by the application of a geometric mean. EA3 is then shown to have some desirable properties. It offers the possibility of emphasizing or the de-emphasizing the maximum values associated to each evidence appraisal via a cautiousness parameter (the thermodynamic β of softmax). Furthermore, EA3 allows one to incorporate the importance of the dimensions of appraisals. Eventually, we show how EA3 can be used to support assessments of causal hypotheses within a Bayesian decision making approach.Drawing on these traditions, we present an 3 represents one of the first attempts to solve the problem of evidence appraisal through an easy-to-exploit numerical measure  and all β > 0 it holds thatProof. The computation is straightforward:β and how the simplest ranking scheme  acts in the simple case in which all appraisals are equal to c, see β, the smaller vf, the further away the curves plotted in c will have an aggregate, vf, equal to less than c. In other words, RWE that is less than perfect in more than one respect has an even lower aggregated appraisal. This seems right, studies which might produce poor evidence for multiple reasons are considered to produce very poor evidence. It is for this reason that we require that β > 0.This observation demonstrates the role of β = +∞ represents maximal cautiousness, if the study is not perfect in all respects (c < 1), then β = 0 represents maximal optimism (and in our eyes overly strong optimism) in that c < 1) is overall as good as just a single imperfection.β ≪ 0, then vf to the unit interval and a different normalisation step would be required.Furthermore, note that if Definition 1 (Monotonicity) We call a functionif and only if the restriction of f to all coordinates is a strictly monotonously increasing function.Proposition 2. For every given fixed ranking schemethe function EA3confirmatory weight  > 1 ≥ x for all x ∈  and all β > 0.We now compute that this is indeed the case:Corollary 1. For every given fixed ranking schemethe function EA3. Bayes’ Theorem is ubiquitous in Bayesian analyses and it is straight-forwardly applied, if the evidence can be taken at face value. In medical inference, where evidence cannot be taken at face value, numerous methodological design features and choices (conscious and subconscious) bear on the information a study provides.H1, …, Hn, i.e. the states of the world. Let us denote the available evidence by Hhunder the assumption that the conditional probabilities are likelihoods specified by statistical models.Consider a set of exhaustive and mutually exclusively statistical hypotheses do not specify the relevant likelihoods for RWE. The challenge hence arises to specify these conditional probabilities. We next show how this can be done via an application of EA3.In medical inference problems with RWE, the calculations of Bayes’ Theorem remain valid, the statistical models however vf = 0, then the posterior H is true or not, this does not change the probability of obtaining How should the posterior probabilities vf > 0.These considerations suggest that vf is interpreted as the probability that the evidence can be taken at face value and 1 − vf can be interpreted as the probability that the evidence is completely uninformative.From a theoretical point of view, one may interpret the convex combination in The modified posterior probability of a hypothesis given one available RWE study is3 to all every study individually, thus obtaining s-many outputs The assumption of a single available RWE study is, of course, rather unrealistic. We now show how to deal with multiple available RWE studies, Under the assumption that the studies have been conducted independently from each other, we can generalise E-Synthesis is a Bayesian framework developed for determining probabilities of particular drugs causing a specific adverse reaction ]-1., p. 11 g. (2019)  is inste. (2019)  this sin P(©|E1)  and Q appropriate values of the ual data . The β-p3 reflects the position of a single agent (or of a unanimous committee). In reality, drug approval or withdrawal decisions are a group effort involving experts from different areas , different appraisals (different 3 into a multi-agent framework which represents different (risk) attitudes, preferences and areas of expertise of stakeholders in drug (un-)safety assessments.Furthermore, EAntatives ), which We expect the assessment and use of RWE for medical inference to continue to grow in coming years, drawing on scientific fields in which there are, by the very nature of the investigation, (next to) no randomised studies. For example, in macroeconomics we cannot simply randomly assign countries into different trial arms to learn about the disputed causal relationships between minimum wages and employment  and in nS1 Text(TXT)Click here for additional data file.


Patients diagnosed with cancer face many challenges and need a good understanding of their diagnosis and proposed treatments to make informed decisions about their care. Health literacy plays an important role in this and low health literacy has been associated with poorer outcomes. The aims of this review are to identify which outcomes relate to health literacy in patients with cancer, and to combine this through a mixed studies approach with the patient experience as described through qualitative studies.Four electronic databases were searched in January 2021 to identify records relating to health literacy and patients with cancer. Records were independently screened then assessed for inclusion by two reviewers according to the following criteria: patients aged ≥18 years with cancer, English language publication AND health literacy measured with validated tool and measured outcome associated with health literacy OR qualitative study exploring the role of health literacy as patients make decisions about health. Quality was independently assessed by two reviewers. A narrative synthesis was performed, and findings integrated through concept mapping. This systematic review was registered with PROSPERO, entry CRD42020166454.4441 records were retrieved. Following de-duplication, 2496 titles and abstracts were screened and full texts of 405 papers were reviewed for eligibility. 66 papers relating to 60 studies met the eligibility criteria. Lower health literacy was associated with greater difficulties understanding and processing cancer related information, poorer quality of life and poorer experience of care. Personal and situational influences contributed to how participants processed information and reached decisions about their care.This review highlights the important role of health literacy for patients with cancer. Outcomes are poorer for those who experience difficulties with health literacy. Further efforts should be made to facilitate understanding, develop health literacy and support patients to become more involved in their care. Patients with cancer are expected to understand complicated information about their diagnosis and management. They must learn a new language of health terminology, provide consent for treatments and procedures, turn up at the right time and place for their appointments and seek help in an appropriate way and in a timely manner. Health literacy is integral to this.Health literacy has been defined as “the combination of personal competencies and situational resources needed for people to access, understand, appraise and use information and services to make decisions about health. It includes the capacity to communicate, assert and act upon these decisions” . Two disAs a ‘risk’, health literacy is associated with hospitalisation, use of emergency care, uptake of preventative services, ability to understand health information and take medications appropriately, and, in older people, with health status and mortality . CrucialIn addition to relationships with health outcomes, health literacy is a prerequisite for shared and informed decision making , 8 and hThis paper aims to provide an up to date overview of the literature, enabling us to understand the clinical relevance of health literacy in cancer care more broadly than existing reviews focussing on self-management behaviours , limitedThe review protocol was prospectively registered with the International Prospective Register for Systematic Reviews (PROSPERO), entry number CRD42020166454. Wording of the qualitative objective has been refined since registration.Searches were carried out on four electronic databases  in January 2021. Publications addressing cancer and health literacy were sought using search terms identified through review of the existing literature, including MeSH terms  and keywords . Specific outcomes were not stipulated due to the anticipated varied nature of the studies. The search strategy was reviewed by an experienced librarian and is shown in Screening of titles and abstracts was undertaken by two independent reviewers, with one screening all papers (CH) and three reviewers screening a third of papers each , with a preference for inclusion if there were disagreements. Following the initial screening process, full texts of the remaining studies were obtained and independently reviewed for eligibility by two authors  according to the following criteria:Patients aged ≥18 years with malignancy of any site English languageHealth literacy assessed with validated tool Measured outcomes associated with health literacyQuantitative papers:Studies exploring the role of health literacy as patients access, understand, appraise and use information and services to make decisions about healthQualitative papers:Use of the term ‘health literacy’ but referring to disease specific knowledge onlyCase reports, review papers, conference proceedings, opinion pieces, editorials, letters to the editor, dissertations/theses, book chapters, protocolsAt all stages, disagreements were resolved through discussion.One reviewer (CH) extracted data from all papers, with independent extraction from eight papers by a second reviewer  to check for accuracy. Data on study characteristics , sample , outcomes  and qualitative methods used, data analysis procedure, key themes and findings and participant quotes were collected.Quality was assessed using the Mixed Methods Appraisal Tool (MMAT) , allowinStatistical pooling of data was not performed due to the varied study designs, outcomes, health literacy assessment tools and thresholds used to identify participants with lower health literacy. Drawing on guidance developed by Popay et al. , a narraAfter extraction of the data, studies were grouped and tabulated based on the two review objectives. To address the second, qualitative objective, a thematic analysis was performed . The keyFinally, relationships between studies across both objectives were explored through concept mapping, again drawing on existing models as appropriate , 8, and 4440 records were retrieved from the searches . After rFifty-eight papers relating to 52 studies addressed this objective, of which 49 studies were of quantitative non-randomised design. The majority were conducted in the USA (31/52), and the most common health literacy assessment tools were variations on the Chew screening questions (16/52) and Rapid Estimate of Adult Literacy in Medicine (REALM) (12/52). Breast (N = 12) and prostate cancer (N = 8) were the most studied individual tumour sites, and a further 21 studies included participants with a variety of cancer diagnoses. Thirty-two papers were considered higher quality (MMAT score 4/5 or 5/5). See N = 1060) study of patients with newly diagnosed breast cancer, although confounding variables were not controlled for  help you steer your treatment? Participant: By giving me information about how each of the approaches is and how it works, the long-term and short-term effects of each, the certainties and uncertainties around each one, and certainly the cure rate” (59 year old attendee at prostate cancer support group for three years)  were and he was reluctant to tell me, but finally he agreed, and I went, and I never went back to this guy and I started my search and the prostate cancer groups were very instrumental in helping me to make my decision. They gave me knowledge.” (63 year old attendee at prostate cancer support groups for 10 years) [Conversely, those who had developed a good understanding and the confidence to do so were able to effectively navigate the healthcare system and exercise their rights to ensure that they received ‘good care’: 0 years) . Patient0 years) .The concept map below , shows tPatients with lower health literacy may have more difficulty understanding and recalling the information they have been given, demonstrate lower knowledge and have higher unmet information needs , engage more with health promoting activities such as exercise, and experience a better quality of life (‘Outcomes’). They may be more likely to seek out the additional information they need, perhaps learning to interpret numbers relating to their condition or finding opportunities to learn from others (‘Processes’). With greater understanding and knowledge of their disease and their rights, they may take a more active role in making decisions about their care and have greater confidence in navigating the system (‘Outcomes’).The findings from this mixed studies systematic review demonstrate the role and consequences of health literacy in the oncology setting. The outcomes associated with health literacy are varied, with some having clear implications for care delivery, and others demonstrating the negative impact of health literacy difficulties on the experience of care as reported by patients themselves. While the quantitative data gives evidence for the measurable outcomes associated with health literacy, the qualitative findings complement this by adding the patient voice, identifying some of the influences of health literacy, and offering an insight into some of the associations seen. Findings relating to information processing and decision-making highlight some of the ‘Processes’ affected by these ‘Influences’ and demonstrate how health literacy may link to the described ‘Outcomes’. Ensuring that the system is considerate of the burden it places on patients, taking steps to simplify information and processes, providing patients with the confidence and opportunities to speak up, and making support available is therefore essential.Although further empirical work is needed to determine the nature of these associations, the causal links between health literacy and health outcomes have been hypothesised , 85. TheOur review supports the Health Literacy Pathway Model presented by Edwards et al. , which dThe decision-making preferences and degree to which patients with lower health literacy feel able to take on a more active role in the oncology setting require further study. But whether a patient wishes to be actively involved in decision making or prefers to be guided by their clinician, an understanding of the aims and potential risks of treatment are key to informed consent . The Genhealth literacy remained the subject of interest, but other studies may have been missed. Secondly, to achieve consistency in a field with a range of measures, it was agreed that only those health literacy assessment tools used in their validated form would be included. Although this excluded some studies using non-validated adaptations, it was deemed important in order to be able to draw any comparisons between studies. As found elsewhere, the range of health literacy measures and identification of participants with lower health literacy makes such comparisons difficult. Over half of the included studies were conducted in the USA, with none carried out in the UK, which may limit the relevance of some results to other healthcare settings.One limitation of this review is the exclusion of studies using measures relating to health literacy but referring only to literacy. This was to ensure that A major strength of the review is the use of the mixed methods approach, bringing together a more comprehensive picture of health literacy in the oncology setting, incorporating the patient voice and allowing us to better understand the experience from the patients’ perspective. The broad inclusion criteria allowed us to identify the association between health literacy and a wider range of outcomes than has previously been addressed , 11. AddHealth literacy plays a key role in cancer care, with important implications for patient experience and outcomes. Those with lower health literacy face greater difficulties processing information, report poorer psychological outcomes and experience a poorer quality of life, whilst those with higher health literacy appear better informed and able to take on a more active role in managing their health. Future interventions aimed at supporting person centred care in this setting should therefore take account of health literacy and consider the factors influencing its development. Further research is required to better understand the decision making processes and preferences of those with lower health literacy receiving care for cancer.S1 Table(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S1 Checklist(DOCX)Click here for additional data file.