Continuity of Care among People Experiencing Homelessness and Mental Illness: Does Community Follow‐up Reduce Rehospitalization?
Bibliographic record
Abstract
OBJECTIVE: To examine whether timely outpatient follow-up after hospital discharge reduces the risk of subsequent rehospitalization among people experiencing homelessness and mental illness. DATA SOURCES: Comprehensive linked administrative data including hospital admissions, laboratory services, and community medical services. STUDY DESIGN: Participants were recruited to the Vancouver At Home study based on a-priori criteria for homelessness and mental illness (n = 497). Logistic regression analysis was used to assess the relationship between outpatient care within 7 days postdischarge and subsequent rehospitalization over a 1-year period. DATA EXTRACTION: Data were extracted for a consenting subsample of participants (n = 433) spanning 5 years prior to study enrollment. PRINCIPAL FINDINGS: More than half of the eligible sample (53 percent; n = 128) were rehospitalized within 1 year following an index hospital discharge. Neither outpatient medical services nor laboratory services within 7 days following discharge were associated with a significantly reduced likelihood of rehospitalization within 2 months (AOR = 1.17 [CI = 0.94, 1.46]), 6 months (AOR = 1.00 [CI = 0.82, 1.23]) or 12 months (AOR = 1.24 [CI = 1.02, 1.52]). CONCLUSIONS: In contrast to evidence from nonhomeless samples, we found no association between timely outpatient follow-up and the likelihood of rehospitalization in our homeless, mentally ill cohort. Our findings indicate a need to address housing as an essential component of discharge planning alongside outpatient care.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".