Developing and testing an intervention to prevent homelessness among individuals discharged from psychiatric wards to shelters and ‘No Fixed Address’
Bibliographic record
Abstract
Shelter data in a recent study revealed discharges from psychiatric facilities to shelters or the street occurred at least 194 times in 2002 in London, Ontario, Canada. This problem must be addressed to reduce the disastrous effects of such discharge, including re-hospitalization and prolonged homelessness. An intervention was developed and tested to prevent homelessness associated with discharge directly to no fixed address. A total of 14 participants at-risk of being discharged without housing were enrolled, with half randomized into the intervention group. The intervention group was provided with immediate assistance in accessing housing and assistance in paying their first and last month's rent. The control group received usual care. Data was collected from participants prior to discharge, at 31 and 6-months post-discharge. All the individuals in the intervention group maintained housing after 3 and 6 months. All but one individual in the control group remained homeless after 3 and 6 months. The exception joined the sex trade to avoid homelessness. The results of this pilot were so dramatic that randomizing to the control group was discontinued. Discussions are underway to routinely implement the intervention. Systemic improvements can prevent homelessness for individuals being discharged from psychiatric wards.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".