Examining the Characteristics of Interventions Targeting Mental Health Stigma among Athletes: A Systematic Review
Bibliographic record
Abstract
Researchers’ interest in mental health stigma (MHS) in sport has increased given its prominence as a barrier to help-seeking. Yet, despite existing interventions, the evidence base targeting reductions in MHS in athletes is equivocal. The purpose of this systematic review is to describe the core components of interventions developed to target MHS in athletes. Pre-registered on PROSPERO (CRD42023470267), the review adheres to PRISMA guidelines. Inclusion criteria were: 1) Studies focusing on interventions targeting MHS delivered exclusively to athletes regardless of age or competitive level; 2) Quasi-experimental, or randomized control trials and 3) articles published in English. A comprehensive keyword search across six databases (PsycINFO, MEDLINE via PubMed, CINAHL, Proquest Nursing and Allied Health, SPORTDiscus, and Web of Science) yielded 665 studies for screening, with seven meeting inclusion criteria including nine interventions. Methodological quality was assessed via the Quality Assessment Tool for Quantitative Studies or the Cochrane Collaboration tool depending on study design. Across retained studies, general study details and sample characteristics were coded. The TIDieR checklist was used to detail intervention reporting with enrichments recommended by Clay et al. (2020) MHS intervention framework. MHS intervention components were coded by two independent reviewers. Results indicated that while certain aspects of intervention reporting, such as procedures, were consistently well-documented, deficiencies existed in the other items like reporting fidelity, modifications, and tailoring/cultural adaptation. The discussion highlights the significance of providing a complete description of the intervention's components, which may make it easier for future researchers to replicate, implement, and disseminate research findings in the context of knowledge mobilization. While the current review offers a unique focus on MHS in athletes, it has limitations to consider, including a narrow search focused on English studies, missing data retrieval, and the TIDieR checklist's limited assessment of intervention quality and effectiveness. These limitations call for careful interpretation of the review's findings and highlight areas for improvement in future research. The authors did not receive any funding from any organization for the current review.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".