Trajectories of recovery among homeless adults with mental illness who participated in a randomised controlled trial of Housing First: a longitudinal, narrative analysis
Bibliographic record
Abstract
OBJECTIVES: This study used longitudinal, narrative data to identify trajectories of recovery among homeless adults with mental illness alongside the factors that contribute to positive, negative, mixed or neutral trajectories over time. We expected that participants who received Housing First (HF) would describe more positive trajectories of recovery than those who were assigned to Treatment as Usual (TAU; no housing or support provided through the study). DESIGN: Narrative interview data were collected from participants at baseline and 18 months after random assignment to HF or TAU. SETTING: Participants were sampled from the community in Vancouver, British Columbia. PARTICIPANTS: Fifty-four participants were randomly and purposively selected from the larger trial; 52 were interviewed at baseline and 43 were reinterviewed 18 months after randomisation. METHOD: Semistructured interviews were conducted at both time points. For each participant, paired baseline and follow-up narratives were classified as positive, negative, mixed or neutral trajectories of recovery, and thematic analysis was used to identify the factors underlying different trajectories. RESULTS: Participants assigned to HF (n=28) were generally classified as positive or mixed trajectories; those assigned to TAU (n=15) were generally classified as neutral or negative trajectories. Positive trajectories were characterised by a range of benefits associated with good-quality, stable housing (eg, reduced substance use, greater social support), positive expressions of identity and the willingness to self-reflect. Negative, neutral and mixed trajectories were characterised by hopelessness ('things will never get better') related to continued hardship (eg, eviction, substance use problems), perceived failures and loss. CONCLUSIONS: HF is associated with positive trajectories of recovery among homeless adults with mental illness. Those who did not receive housing or support continued to struggle across a wide range of life domains. Findings are discussed with implications for addressing services and broader social change in order to benefit this marginalised population.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".