Preventing Discharge to No Fixed Address – Version 2:
Bibliographic record
Abstract
The “No Fixed Address” version 2 (NFAv.2) project tested the efficacy of a potential best practice program that aimed to prevent discharge from hospital into homelessness. Forchuk and colleagues developed a system that streamlined housing and social supports using on-site access to help inpatients in a psychiatric unit who were homeless or at-risk of homelessness find safe, affordable housing. A total of 370 individuals accessed the NFAv.2 program between August 2017 and May 2020. Of these, 88 participants who accessed the intervention were enrolled in the evaluation of the project. Information on housing history and housing outcomes were collected during hospital admission and at 3-time points post-discharge. Focus groups were conducted for program participants, health care staff, and community partners to gather information regarding their experiences with the program. Of those who participated in the intervention, 80% were housed and remained housed at 12 months post-discharge. Results from focus groups also indicated that the majority of NFAv.2 clients, staff, and community partners were satisfied with the intervention. Since homelessness has a detrimental effect on recovery, client safety, and healthcare expenditures, locating safe housing has had a positive impact on treatment, rehabilitation, and the healthcare system as a whole. The findings of this project offer policy alternatives for the prevention of homelessness for at-risk individuals.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".