Safer spaces in youth development programs and health in Canadian youth
Bibliographic record
Abstract
Engagement in youth programs is a potential means to promote health and well-being across populations of young people. Safer spaces in these youth programs are likely critical in fostering positive health outcomes, but current research on the links between safer spaces and health is limited. In this exploratory study, we examined links between program safety in youth development programs and minoritized status, and health-related quality of life (HRQoL) and psychosomatic health complaints. Participants (N = 282; Mean age = 16.97 years; SD = 2.97) self-identified across various minority status groups, including LGBTQ (30%) and a range of perceived income levels. We tested a statistical model in which safer spaces, LGBTQ status and perceived income predicted HRQoL and health complaints in youth development program participants. LGBTQ status and lower perceived income were related to lower HRQoL and more health complaints, and safer space in youth development programs was related to better HRQoL. We also found an interaction effect, such that safer spaces in youth programs appeared to be especially beneficial for HRQoL for youth with higher incomes. Findings reinforce past research on LGBTQ status and income as factors for youth wellness and mental health. Findings also suggest that perceived safer spaces in youth development programs support better HRQoL and lower health complaints, across populations of participating youth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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".