Advancing recovery education for people experiencing housing instability: A qualitative analysis of service user and provider perspectives in Canada
Bibliographic record
Abstract
The recovery paradigm is increasingly being adopted within mental health services internationally, to support a process of personal change for affected individuals, with the aim of living a satisfying, hopeful, contributing life within the limitations of mental illness. In efforts to assist the process of recovery, Recovery Education Centres (REC), offering recovery supports through education rather than traditional service models, have been widely implemented; there is limited evidence to date with respect to the experiences and outcomes of disadvantaged populations, such as people experiencing homelessness, with recovery education. This study used qualitative methods to explore the perspectives and experiences of service users and providers of the Supporting Transitions and Recovery (STAR) Learning Centre in Toronto, Canada, focused on supporting the process of recovery for individuals with histories of homelessness. Between July 2017 and June 2018, semistructured interviews with 20 service users, one key informant, and a focus group comprising eight current and former REC staff and volunteers were conducted to explore opportunities to better tailor the curriculum, and key programmatic features, to the needs of the population. Interviews were analysed using inductive thematic analysis. Most participants described favourable experiences with the REC and suggested adding more challenging course content (n = 10) and increasing delivery options (n = 8), highlighting the diversity of participant needs. Others described the importance of improving accessibility through geographic expansion, offering transit subsidies, and using innovative media. In addition, the importance of delivering more courses in partnership with community partners, to maximise reach and impact, was also highlighted (n = 15). This study underscores the importance of engaging service users and providers, including peers, in ongoing adaptations to best serve the target populations. The expertise and lived experience of these key stakeholders offer a unique lens, supporting the process of recovery, through co-production of curriculum content and joint program planning and improvements. Findings can inform REC development for disadvantaged populations and potentially enhance recovery outcomes for those experiencing multiple barriers to recovery.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".