Physical activity participation and mental health profiles in Canadian medical students: Latent profile analysis using continuous latent profile indicators
Bibliographic record
Abstract
Previous research on medical students' 'mental health' typically focuses on mental illness with minimal focus on positive mental health indicators, such as well-being and resilience. One malleable predictor of mental health is physical activity (Ravindran et al., 2016); though research on the relationship between different intensities of physical activity and mental health is inconsistent and none have included medical students. The primary purpose of this study was to examine relationships between physical activity, including mild and moderate-to-vigorous physical activity (MVPA), and mental health profiles in a sample of Canadian medical students. A total of N = 125 medical students completed an online survey. Latent profile analysis was performed in Mplus to identify distinct profiles using four continuous latent profile indicators (emotional well-being, social well-being, psychological well-being and resilience). The AUXILIARY function was used to test for differences in physical activity intensities between profiles. Three mental health profiles emerged, showing low (n = 18), moderate (n = 72) and high (n = 36) mental health. The classification quality was good (entropy = 0.81). Individuals in the high mental health profile participated in more mild physical activity (M = 144.28 mins/week; SD = 22.12) and less MVPA (M = 195.86 mins/week; SD = 25.67) compared to students in the moderate and low profiles, though not significantly. This suggests that mild physical activity might be the most effective intensity in supporting mental health among medical students.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".