“I feel like a kid again”: the voices of youth experiencing homelessness in a mobile recreation program
Bibliographic record
Abstract
Youth homelessness is at an all-time high in Canada and is a complex social issue. The vast majority of interventions and research focuses on addressing the immediate needs of people experiencing homelessness, such as housing, harm reduction (due to substance use, violence, and crime) food security, and illness. The important role that recreation, sport, and physical activity play in the lives of youth is well established, however, the experiences of homeless youth in these spaces are relatively unknown. Thus, in partnership with a not-for-profit organisation and emergency youth shelters, this research study explored homeless youths’ experience of participating in a mobile recreation-based program and how this experience impacted their everyday lives. Ten youth currently residing in emergency shelters who participated in the program engaged in either one-on-one or group interviews. A qualitative content analysis approach was employed and three key themes were identified: (1) creating safe social spaces and cultivating relationships, (2) reconnecting to previous passions and meaning, and (3) promoting wellness. Findings suggest that the recreation program provided unique and layered experiences for the youth participants and had profound impacts on their overall wellbeing. Implications for practitioners and policy makers are offered.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".