How does music contribute to well-being? Perspectives from homeless young adults with problematic psychoactive substance use
Bibliographic record
Abstract
Homeless young adults with problematic psychoactive substance (PS) use face multiple challenges that compromise their well-being. Despite these challenges, few of them access psychosocial services and rather rely on personal resources to promote their own well-being. However, literature has been largely centered on their difficulties, leaving their strengths and capacities unknown. Despite this lack of knowledge, literature suggests that music is very important in the lives of young adults, especially because it helps them meet multiple well-being needs. The objective of this study was to describe and understand the perspectives of homeless young adults who experience problematic PS use on the ways music contributes to their well-being. Fifteen participants took part in semi-directed qualitative interviews that covered their experiences regarding the role of music in well-being. We also performed an iterative thematic analysis. Results highlight the marked importance of music for participants. Our study also demonstrates participants' capacity to adapt their use of music to address the emotional, psychological, and social challenges they face. This study contributes to a better understanding of young adults' use of music in promoting their global well-being and to the development of adapted outreach interventions that account for their capacities and interests.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".