The Effect of Jack Chapters on the Mental Health Help Seeking Attitudes of Canadian Secondary Students in the COMPASS Study
Bibliographic record
Abstract
Background: School-based peer-led mental health literacy (MHL) programs may be effective tools to reduce stigma, and improve MHL and help seeking attitudes in adolescent populations. The objectives of this thesis were to evaluate the effectiveness of Jack Chapters—a school-based peer-led MHL program—on the mental health help seeking attitudes of Canadian secondary students at the (1) school- and (2) student-level in all students, and (3) in students with anxiety and/or depression. Methods: Linked survey data from the COMPASS study collected during the 2017/18 (T1) and 2018/19 (T2) academic years study were used. The total sample included 5808 students at 30 Ontario secondary schools, of which 3194 students had clinically-relevant anxiety and/or depression scores. COMPASS school-level and Jack Chapters data were used to determine the intervention schools (i.e., schools that implemented Jack Chapters at both timepoints). Four control schools were matched per one intervention school based on school size and urbanicity. A t-test was used to compare the difference in help-seeking reluctancy at the school-level by study group and between timepoints. Generalized logistic mixed models were used to determine the effectiveness of the Jack Chapters program on help seeking attitudes at the student-level between timepoints. Results: At the school-level, there was a significantly greater increase in help seeking reluctancy between intervention schools compared to control schools from T1 to T2. In both the total sample and the subsample, there were higher odds of students being reluctant to seek help at T2 if they were reluctant at T1, however, study group was not a significant predictor of this relationship. Having lower family and friend support were associated with an increase in reluctancy at T2 for both the total and subsample populations, and in the total sample, an increase in school connectedness was associated with a decrease in reluctancy at T2. Conclusions: This study adds to the sparse literature on the effectiveness of improving school-based peer-led MHL initiatives on mental health help seeking attitudes among adolescent populations. Despite the strengths of Jack Chapters, our null results indicate that programming can improve. Findings may inform program targeting for specific populations within the school.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".