The effect of a Housing First intervention on primary care retention among homeless individuals with mental illness
Bibliographic record
Abstract
BACKGROUND: Primary care retention, defined as ongoing periodic contact with a consistent primary care provider, is beneficial for people with serious chronic illnesses. This study examined the effect of a Housing First intervention on primary care retention among homeless individuals with mental illness. METHODS: Two hundred individuals enrolled in the Toronto site of the At Home Project and randomized to Housing First or Treatment As Usual were studied. Medical records were reviewed to determine if participants were retained in primary care, defined as having at least one visit with the same primary care provider in each of two consecutive six-month periods during the 12 month period preceding and following randomization. RESULTS: Medical records were obtained for 47 individuals randomized to Housing First and 40 individuals randomized to Treatment As Usual. During the one year period following randomization, the proportion of Housing First and Treatment As Usual participants retained in primary care was not significantly different (38.3% vs. 47.5%, p = 0.39). The change in primary care retention rates from the year preceding randomization to the year following randomization was +10.6% in the Housing First group and -5.0% in the Treatment As Usual group. CONCLUSION: Among homeless individuals with mental illness, Housing First did not significantly affect primary care retention over the follow-up period. These findings suggest Housing First interventions may need to place greater emphasis on connecting clients with primary care providers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".