The effectiveness of a pilot peer-based physical activity mentoring program to promote mental health on campus
Bibliographic record
Abstract
The pervasiveness of psychological concerns among university students has been deemed a mental health crisis. As such, campus mental health services necessitate sustainable and feasible promotional strategies targeted towards prevention. Campus-based physical activity interventions may be effective at promoting student mental health. As such, the purpose of the present investigation was to evaluate the effectiveness of two pilot yearlong peer-to-peer physical mentoring programs in improving indices of mental health (e.g., resilience, anxiety symptoms, depressive symptoms). First-year students participated in Study 1 (n = 79; 70.9% female; Mage = 20.00 ± .77) and Study 2 (n = 217; 67.3% female; Mage = 19.13 ± 1.19). In both studies, students demonstrated improved levels of resilience (Study 1: F(1.77, 99.24) = 2.77, p = .07, ?p2 = .05; Study 2: t(109) = -1.71, p = .09, d = .15) and lower levels of anxiety (Study 1: F(1.90, 110.36) = 3.88, p = .03, ?p2 = .06; Study 2: t(109) = 1.70, p = .09, d = .14) between baseline and post-program. However, only anxiety scores differed significantly in Study 1. Evaluations suggest that a physical activity peer-mentor program is feasible to implement on campus and is a potential strategy improve mental well-being. However, program characteristics and structure may need to be further reviewed in order to evaluate changes in mental health more broadly, and mechanisms for anxiety specifically. Further research efforts are needed to address gaps in empirical knowledge and advance this unique peer-based intervention to a campus-wide mental health promotion strategy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".