Story telling: A narrative based evaluation of supported housing for consumers at Waterloo Regional Homes for Mental Health, Inc.
Bibliographic record
Abstract
A qualitative, narrative approach was used to evaluate a supported housing program for formerly homeless people with serious mental health problems. The housing organization hosting the research is currently providing supported-living, single-occupancy apartments funded under the Phase II Mental Health Homelessness Initiative by the Ontario Ministry of Health and Long-Term Care. The study was designed to gather the stories of those who have been recently homeless or at risk for homelessness, have a serious mental illness, and have been housed within the past year in this housing, as well as to provide an evaluation of the effectiveness of supported housing for this particular population. Narratives were developed based on twelve interviews, six with consumers and six with a significant other chosen by each consumer. The consumer and significant other interviews were combined into one narrative for each participant. The main analysis involved a comparison of the lives of the consumers before they entered supported housing and after they entered supported housing. The narrative approach was used to complement quantitative outcome research that has been carried out to examine the effectiveness of supported housing. Before supported housing, consumers reported their housing to be unstable; they felt insecure financially; many relationships with family were often strained; and many feared for their physical safety in past housing situations. After supported housing consumers were more stable in their housing; they felt more secure financially; relationships with friends and family were stronger; and they felt safer in their homes. Loneliness was a predominant theme of consumers' experiences of living in independent apartments. The findings were discussed in terms of previous research and their implications for future research and action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".