Investigating the immediate and sustained effect of online mental health literacy training in intercollegiate sport
Bibliographic record
Abstract
Lack of mental health literacy (MHL) is a growing concern in university populations, specifically within the athletic departments. Participating in varsity athletics puts student-athletes at risk for experiencing severe cases of mental distress. Previous research suggests there are multiple limitations when studying MHL intervention programs in sports. These limitations make it difficult to draw clinical significance of the interventions used as they lack rigour, consistency, and online variations. The purpose of this study is to address the gaps in the literature by assessing the effectiveness over time of an online mental health in sport module in improving mental health literacy, stigma, and help-seeking behaviours in a sample of Canadian intercollegiate student-athletes. Five (4 females, 1 male) completed the revised version of the multicomponent mental health literacy measure, self-stigma of seeking help, and the general help-seeking questionnaire prior to and after participating in the “Supporting Student-Athletes Mental Wellness” online module for the student-athlete. Calculation and graphing of the means demonstrated that there was an effect of participating in the module on mental health literacy which were sustained at the 2-month follow-up. Stigma was found to have an immediate improvement after participation in the module. There were no sustained effects found for stigma and help seeking after completion of the training.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".