Determining Associations Between Intervention Amount and Outcomes for Young Autistic Children: A Meta-Analysis.

Importance: Health professionals routinely recommend intensive interventions (ie, 20-40 hours per week) for autistic children. However, primary research backing this recommendation is sparse and plagued by methodological flaws. Objective: To examine whether different metrics of intervention amount are associated with intervention effects on any developmental domain for young autistic children. Data Sources: A large corpus of studies taken from a recent meta-analysis (with a search date of November 2021) of early interventions for autistic children. Study Selection: Studies were eligible if they reported a quasi-experimental or randomized clinical trial testing the effects of a nonpharmacological intervention on any outcome in participant samples comprising more than 50% autistic children 8 years or younger. Data Extraction and Synthesis: Data were independently extracted by multiple coders. Meta-regression models were constructed to determine whether each index of intervention amount was associated with effect sizes for each intervention type, while controlling for outcome domain, outcome proximity, age of participants, study design, and risk of detection bias. Data were analyzed from June 2023 to February 2024. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Main Outcomes and Measures: The primary predictor of interest was intervention amount, quantified using 3 different metrics (daily intensity, duration, and cumulative intensity). The primary outcomes of interest were gains in any developmental domain, quantified by Hedges g effect sizes. Results: A total of 144 studies including 9038 children (mean [SD] age, 49.3 [17.2] months; mean [SD] percent males, 82.6% [12.7%]) were included in this analysis. None of the meta-regression models evidenced a significant, positive association between any index of intervention amount and intervention effect size when considered within intervention type. Conclusions and Relevance: Findings of this meta-analysis do not support the assertion that intervention effects increase with increasing amounts of intervention. Health professionals recommending interventions should be advised that there is little robust evidence supporting the provision of intensive intervention.

The efficacy of combining cognitive training and noninvasive brain stimulation: A transdiagnostic systematic review and meta-analysis.

Over the past decade, an increasing number of studies investigated the innovative approach of supplementing cognitive training (CT) with noninvasive brain stimulation (NIBS) to increase the effects on outcomes. In this review, we aim to summarize the evidence for this treatment combination. We identified 72 published and unpublished studies (reporting 773 effect sizes), including 2,518 participants from healthy and clinical populations indexed in PubMed, MEDLINE, APA PsycInfo, ProQuest, Web of Science, and https://ClinicalTrials.gov (last search: August 9, 2022) that compared the effects of NIBS combined with CT on cognitive, symptoms, and everyday functioning to CT alone at postintervention and/or follow-up. We performed random-effects meta-analyses with robust variance estimation and assessed risk of bias with the Cochrane ROB tool. Only four studies had low risk of bias in all domains, and many studies lacked standard controls such as keeping the outcome assessor and trainer unaware of the treatment condition. Following sensitivity analyses, only learning/memory robustly improved significantly more when CT was combined with NIBS compared to CT only (g = 0.18, 95% CI [0.07, 0.29]) at postintervention, but not in the long term. The effect was small and limited by substantial heterogeneity. The other seven cognitive outcome domains, symptoms, and everyday functioning did not benefit from adding NIBS to CT. Given the methodological limitation of prior studies, more high-quality trials that focus on the potential of combining NIBS and CT to enhance benefits in everyday functioning in the short and long term are needed to evaluate whether combining NIBS and CT is relevant for clinical practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

High replicability of newly discovered social-behavioural findings is achievable.

Failures to replicate evidence of new discoveries have forced scientists to ask whether this unreliability is due to suboptimal implementation of methods or whether presumptively optimal methods are not, in fact, optimal. This paper reports an investigation by four coordinated laboratories of the prospective replicability of 16 novel experimental findings using rigour-enhancing practices: confirmatory tests, large sample sizes, preregistration and methodological transparency. In contrast to past systematic replication efforts that reported replication rates averaging 50%, replication attempts here produced the expected effects with significance testing (P < 0.05) in 86% of attempts, slightly exceeding the maximum expected replicability based on observed effect sizes and sample sizes. When one lab attempted to replicate an effect discovered by another lab, the effect size in the replications was 97% that in the original study. This high replication rate justifies confidence in rigour-enhancing methods to increase the replicability of new discoveries.

Neither neural networks nor the language-of-thought alone make a complete game.

Cognitive science has evolved since early disputes between radical empiricism and radical nativism. The authors are reacting to the revival of radical empiricism spurred by recent successes in deep neural network (NN) models. We agree that language-like mental representations (language-of-thoughts [LoTs]) are part of the best game in town, but they cannot be understood independent of the other players.

Between-case standardized mean differences: Flexible methods for single-case designs.

Single-case designs (SCDs) are a class of research methods for evaluating the effects of academic and behavioral interventions in educational and clinical settings. Although visual analysis is typically the first and main method for analysis of data from SCDs, quantitative methods are useful for synthesizing results and drawing systematic generalizations across bodies of single-case research. Researchers who are interested in synthesizing findings across SCDs and between-group designs might consider using the between-case standardized mean difference (BC-SMD) effect size, which aims to put results from both types of studies into a common metric. Currently available BC-SMD methods are limited to treatment reversal designs with replication across participants and across-participant multiple baseline designs, yet more complex designs are sometimes used in practice. In this study, we extend available BC-SMD methods to several variations of the multiple baseline design, including the replicated multiple baseline across behaviors or settings, the clustered multiple baseline design, and the multivariate multiple baseline across participants. For each variation, we describe methods for estimating BC-SMD effect sizes and illustrate our proposed approach by re-analyzing data from a published SCD study.

Systematic Review of Variables Related to Instruction in Augmentative and Alternative Communication Implementation: Group and Single-Case Design.

PURPOSE: This article provides a systematic review and analysis of group and single-case studies addressing augmentative and alternative communication (AAC) intervention with school-aged persons having autism spectrum disorder (ASD) and/or intellectual/developmental disabilities resulting in complex communication needs (CCNs). Specifically, we examined participant characteristics in group-design studies reporting AAC intervention outcomes and how these compared to those reported in single-case experimental designs (SCEDs). In addition, we compared the status of intervention features reported in group and SCED studies with respect to instructional strategies utilized. PARTICIPANTS: Participants included school-aged individuals with CCNs who also experienced ASD or ASD with an intellectual delay who utilized aided or unaided AAC. METHOD: A systematic review using descriptive statistics and effect sizes was implemented. RESULTS: Findings revealed that participant features such as race, ethnicity, and home language continue to be underreported in both SCED and group-design studies. Participants in SCED investigations more frequently used multiple communication modes when compared to participants in group studies. The status of pivotal skills such as imitation was sparsely reported in both types of studies. With respect to instructional features, group-design studies were more apt to utilize clinical rather than educational or home settings when compared with SCED studies. In addition, SCED studies were more apt to utilize instructional methods that closely adhered to instructional features more typically characterized as being associated with behavioral approaches. CONCLUSION: The authors discuss future research needs, practice implications, and a more detailed specification of treatment intensity parameters for future research.

Annotation of Trauma-related Linguistic Features in Psychiatric Electronic Health Records for Machine Learning Applications.

Psychiatric electronic health records (EHRs) present a distinctive challenge in the domain of ML owing to their unstructured nature, with a high degree of complexity and variability. This study aimed to identify a cohort of patients with diagnoses of a psychotic disorder and posttraumatic stress disorder (PTSD), develop clinically-informed guidelines for annotating these health records for instances of traumatic events to create a gold standard publicly available dataset, and demonstrate that the data gathered using this annotation scheme is suitable for training a machine learning (ML) model to identify these indicators of trauma in unseen health records. We created a representative corpus of 101 EHRs (222,033 tokens) from a centralized database and a detailed annotation scheme for annotating information relevant to traumatic events in the clinical narratives. A team of clinical experts annotated the dataset and updated the annotation guidelines in collaboration with computational linguistic specialists. Inter-annotator agreement was high (0.688 for span tags, 0.589 for relations, and 0.874 for tag attributes). We characterize the major points relating to the annotation process of psychiatric EHRs. Additionally, high-performing baseline span labeling and relation extraction ML models were developed to demonstrate practical viability of the gold standard corpus for ML applications.

Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data.

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Grounding human-object interaction to affordance behavior in multimodal datasets.

While affordance detection and Human-Object interaction (HOI) detection tasks are related, the theoretical foundation of affordances makes it clear that the two are distinct. In particular, researchers in affordances make distinctions between J. J. Gibson's traditional definition of an affordance, "the action possibilities" of the object within the environment, and the definition of a telic affordance, or one defined by conventionalized purpose or use. We augment the HICO-DET dataset with annotations for Gibsonian and telic affordances and a subset of the dataset with annotations for the orientation of the humans and objects involved. We then train an adapted Human-Object Interaction (HOI) model and evaluate a pre-trained viewpoint estimation system on this augmented dataset. Our model, AffordanceUPT, is based on a two-stage adaptation of the Unary-Pairwise Transformer (UPT), which we modularize to make affordance detection independent of object detection. Our approach exhibits generalization to new objects and actions, can effectively make the Gibsonian/telic distinction, and shows that this distinction is correlated with features in the data that are not captured by the HOI annotations of the HICO-DET dataset.

An Examination of Measurement Procedures and Characteristics of Baseline Outcome Data in Single-Case Research.

There has been growing interest in using statistical methods to analyze data and estimate effect size indices from studies that use single-case designs (SCDs), as a complement to traditional visual inspection methods. The validity of a statistical method rests on whether its assumptions are plausible representations of the process by which the data were collected, yet there is evidence that some assumptions-particularly regarding normality of error distributions-may be inappropriate for single-case data. To develop more appropriate modeling assumptions and statistical methods, researchers must attend to the features of real SCD data. In this study, we examine several features of SCDs with behavioral outcome measures in order to inform development of statistical methods. Drawing on a corpus of over 300 studies, including approximately 1,800 cases, from seven systematic reviews that cover a range of interventions and outcome constructs, we report the distribution of study designs, distribution of outcome measurement procedures, and features of baseline outcome data distributions for the most common types of measurements used in single-case research. We discuss implications for the development of more realistic assumptions regarding outcome distributions in SCD studies, as well as the design of Monte Carlo simulation studies evaluating the performance of statistical analysis techniques for SCD data.

Participant characteristics predicting communication outcomes in AAC implementation for individuals with ASD and IDD: a systematic review and meta-analysis.

This meta-analysis examined communication outcomes in single-case design studies of augmentative and alternative communication (AAC) interventions and their relationship to participant characteristics. Variables addressed included chronological age, pre-intervention communication mode, productive repertoire, and pre-intervention imitation skills. Investigators identified 114 single-case design studies that implemented AAC interventions with school-aged individuals with autism spectrum disorder and/or intellectual disability. Two complementary effect size indices, Tau(AB) and the log response ratio, were applied to synthesize findings. Both indices showed positive effects on average, but also exhibited a high degree of heterogeneity. Moderator analyses detected few differences in effectiveness when comparing across diagnoses, age, the number and type of communication modes, participant's productive repertoires, and imitation skills to intervention. A PRISMA-compliant abstract is available: https://bit.ly/30BzbLv.

The Relative Effects of Instruction Linking Word Reading and Word Meaning Compared to Word Reading Instruction Alone on the Accuracy, Fluency, and Word Meaning Knowledge of 4th-5th Grade Students With Dyslexia.

This within-subjects experimental study investigated the relative effects of word reading and word meaning instruction (WR+WM) compared to word-reading instruction alone (WR) on the accuracy, fluency, and word meaning knowledge of 4th-5th graders with dyslexia. We matched word lists on syllables, phonemes, frequency, number of definitions, and concreteness. We assigned half the words to WR and half to WR+WM. Word reading accuracy, word reading fluency, and word meaning knowledge were measured at pretest, immediately following each intervention session, and at posttest, administered immediately following the 12, 45-minute, daily instructional sessions. Compared to WR instruction alone, WR+WM significantly improved accuracy (d = 0.65), fluency (d = 0.43), and word meaning knowledge (d = 1.92) immediately following intervention, and significantly improved accuracy (d = 0.74), fluency (d = 0.84), and word meaning knowledge (d = 1.03) at posttest. Findings support the premise that word meaning knowledge facilitates accurate and fluent word reading, and that instruction explicitly integrating word reading and word meaning may be an effective support for upper elementary students with dyslexia.

Affordance embeddings for situated language understanding.

Much progress in AI over the last decade has been driven by advances in natural language processing technology, in turn facilitated by large datasets and increased computation power used to train large neural language models. These systems demonstrate apparently sophisticated linguistic understanding or generation capabilities, but often fail to transfer their skills to situations they have not encountered before. We argue that computational situated grounding of linguistic information to real or simulated scenarios provide a solution to some of these learning challenges by creating situational representations that both serve as a formal model of the salient phenomena, and contain rich amounts of exploitable, task-appropriate data for training new, flexible computational models. We approach this problem from a neurosymbolic perspective, using multimodal contextual modeling of interactive situations, events, and object properties, particularly afforded behaviors, and habitats, the situations that condition them. These properties are tightly coupled to processes of situated grounding, and herein we discuss we combine neural and symbolic methods with multimodal simulations to create a platform, VoxWorld, for modeling communication in context, and we demonstrate how neural embedding vectors of symbolically-encoded object affordances facilitate transferring knowledge of objects and situations to novel entities, and learning how to recognize and generate linguistic and gestural denotations.

Investigating Narrative Performance in Children With Developmental Language Disorder: A Systematic Review and Meta-Analysis.

PURPOSE: Narrative assessment is one potentially underutilized and inconsistent method speech-language pathologists may use when considering a diagnosis of developmental language disorder (DLD). However, narration research encompasses many varied methodologies. This systematic review and meta-analysis aimed to (a) investigate how various narrative assessment types (e.g., macrostructure, microstructure, and internal state language) differentiate children with typical development (TD) from children with DLD, (b) identify specific narrative assessment measures that result in greater group differences, and (c) evaluate participant and sample characteristics that may influence performance differences. METHOD: Electronic databases (PsycINFO, ERIC, and PubMed) and ASHAWire were searched on July 30, 2019, to locate studies that reported oral narrative language measures for both DLD and TD groups between ages 4 and 12 years; studies focusing on written narration or other developmental disorders only were excluded. We extracted data related to sample participants, narrative task(s) and assessment measures, and research design. Group differences were quantified using standardized mean differences. Analyses used mixed-effects meta-regression with robust variance estimation to account for effect size dependencies. RESULTS: Searches identified 37 eligible studies published between 1987 and 2019, including 382 effect sizes. Overall meta-analysis showed that children with DLD had decreased narrative performance relative to TD peers, with an overall average effect of -0.82 SD, 95% confidence interval [-0.99, -0.66]. Effect sizes showed significant heterogeneity both between and within studies, even after accounting for effect size-, sample-, and study-level predictors. Across model specifications, grammatical accuracy (microstructure) and story grammar (macrostructure) yielded the most consistent evidence of TD-DLD group differences. CONCLUSIONS: Present findings suggest some narrative assessment measures yield significantly different performance between children with and without DLD. However, researchers need to improve consistency of inclusionary criteria, descriptions of sample characteristics, and reporting of correlations between measures to determine which assessment measures reliably distinguish between groups. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21200380.

Multilevel meta-analysis of single-case experimental designs using robust variance estimation.

Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under different conditions. In research areas where SCEDs are prevalent, there is a need for methods to synthesize results across multiple studies. One approach to synthesis uses a multilevel meta-analysis (MLMA) model to describe the distribution of effect sizes across studies and across cases within studies. However, MLMA relies on having accurate sampling variances of effect size estimates for each case, which may not be possible due to auto-correlation in the raw data series. One possible solution is to combine MLMA with robust variance estimation (RVE), which provides valid assessments of uncertainty even if the sampling variances of effect size estimates are inaccurate. Another possible solution is to forgo MLMA and use simpler, ordinary least squares (OLS) methods with RVE. This study evaluates the performance of effect size estimators and methods of synthesizing SCEDs in the presence of auto-correlation, for several different effect size metrics, via a Monte Carlo simulation designed to emulate the features of real data series. Results demonstrate that the MLMA model with RVE performs properly in terms of bias, accuracy, and confidence interval coverage for estimating overall average log response ratios. The OLS estimator corrected with RVE performs the best in estimating overall average Tau effect sizes. None of the available methods perform adequately for meta-analysis of within-case standardized mean differences. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Componential Analysis of English Verbs.

Computational lexical resources such as WordNet, PropBank, VerbNet, and FrameNet are in regular use in various NLP applications, assisting in the never-ending quest for richer, more precise semantic representations. Coherent class-based organization of lexical units in VerbNet and FrameNet can improve the efficiency of processing by clustering similar items together and sharing descriptions. However, class members are sometimes quite different, and the clustering in both can gloss over useful fine-grained semantic distinctions. FrameNet officially eschews syntactic considerations and focuses primarily on semantic coherence, associating nouns, verbs and adjectives with the same semantic frame, while VerbNet considers both syntactic and semantic factors in defining a class of verbs, relying heavily on meaning-preserving diathesis alternations. Many VerbNet classes significantly overlap in membership with similar FrameNet Frames, e.g., VerbNet Cooking-45.3 and FrameNet Apply_heat, but some VerbNet classes are so heterogeneous as to be difficult to characterize semantically, e.g., Other_cos-45.4. We discuss a recent addition to the VerbNet class semantics, verb-specific semantic features, that provides significant enrichment to the information associated with verbs in each VerbNet class. They also implicitly group together verbs sharing semantic features within a class, forming more semantically coherent subclasses. These efforts began with introspection and dictionary lookup, and progressed to automatic techniques, such as using NLTK sentiment analysis on verb members of VerbNet classes with an Experiencer argument role, to assign positive, negative or neutral labels to them. More recently we found the Brandeis Semantic Ontology (BSO) to be an invaluable source of rich semantic information and were able to use a VerbNet-BSO mapping to find fine-grained distinctions in the semantic features of verb members of 25 VerbNet classes. This not only confirmed the assignments previously made to classes such as Admire-31.2, but also gave a more fine-grained semantic decomposition for the members. Also, for the Judgment-31.1 class, the new method revealed new, more fine-grained existing semantic features for the verbs. Overall, the BSO mapping produced promising results, and as a manually curated resource, we have confidence the results are reliable and need little (if any) further hand-correction. We discuss our various techniques, illustrating the results with specific classes.

Semantic Representations for NLP Using VerbNet and the Generative Lexicon.

The need for deeper semantic processing of human language by our natural language processing systems is evidenced by their still-unreliable performance on inferencing tasks, even using deep learning techniques. These tasks require the detection of subtle interactions between participants in events, of sequencing of subevents that are often not explicitly mentioned, and of changes to various participants across an event. Human beings can perform this detection even when sparse lexical items are involved, suggesting that linguistic insights into these abilities could improve NLP performance. In this article, we describe new, hand-crafted semantic representations for the lexical resource VerbNet that draw heavily on the linguistic theories about subevent semantics in the Generative Lexicon (GL). VerbNet defines classes of verbs based on both their semantic and syntactic similarities, paying particular attention to shared diathesis alternations. For each class of verbs, VerbNet provides common semantic roles and typical syntactic patterns. For each syntactic pattern in a class, VerbNet defines a detailed semantic representation that traces the event participants from their initial states, through any changes and into their resulting states. The Generative Lexicon guided the structure of these representations. In GL, event structure has been integrated with dynamic semantic models in order to represent the attribute modified in the course of the event (the location of the moving entity, the extent of a created or destroyed entity, etc.) as a sequence of states related to time points or intervals. We applied that model to VerbNet semantic representations, using a class's semantic roles and a set of predicates defined across classes as components in each subevent. We will describe in detail the structure of these representations, the underlying theory that guides them, and the definition and use of the predicates. We will also evaluate the effectiveness of this resource for NLP by reviewing efforts to use the semantic representations in NLP tasks.

Cluster wild bootstrapping to handle dependent effect sizes in meta-analysis with a small number of studies.

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small-sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models.

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the "metafor" and "clubSandwich" packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

A systematic review and meta-analysis of effects of psychosocial interventions on spiritual well-being in adults with cancer.

Objective: Spiritual well-being (SpWb) is an important dimension of health-related quality of life for many cancer patients. Accordingly, an increasing number of psychosocial intervention studies have included SpWb as a study endpoint, and may improve SpWb even if not designed explicitly to do so. This meta-analysis of randomized controlled trials (RCTs) evaluated effects of psychosocial interventions on SpWb in adults with cancer and tested potential moderators of intervention effects. Methods: Six literature databases were systematically searched to identify RCTs of psychosocial interventions in which SpWb was an outcome. Doctoral-level rater pairs extracted data using Covidence following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Standard meta-analytic techniques were applied, including meta-regression with robust variance estimation and risk-of-bias sensitivity analysis. Results: Forty-one RCTs were identified, encompassing 88 treatment effects among 3883 survivors. Interventions were associated with significant improvements in SpWb (g = 0.22, 95% CI [0.14, 0.29], p < 0.0001). Studies assessing the FACIT-Sp demonstrated larger effect sizes than did those using other measures of SpWb (g = 0.25, 95% CI [0.17, 0.34], vs. g = 0.10, 95% CI [-0.02, 0.23], p = 0.03]. No other intervention, clinical, or demographic characteristics significantly moderated effect size. Conclusions: Psychosocial interventions are associated with small-to-medium-sized effects on SpWb among cancer survivors. Future research should focus on conceptually coherent interventions explicitly targeting SpWb and evaluate interventions in samples that are diverse with respect to race and ethnicity, sex and cancer type.