Housing First - Where is the evidence?
Bibliographic record
Abstract
n the last ten years a radical transformation has occurred in the attitudes and practices guiding housing programs that provide emergency and long-term housing for homeless people. This shift evolved from linear or step-wise models of either coupling housing with treatment, or of requiring treatment prior to obtaining permanent housing (Treatment Continuum – TC) (Padgett, et al., 2006), to a priority placed on housing without treatment expectations (Brown, 2005). The latter approach has been labelled housing first (HF) and has rapidly acquired wide-spread adoption by communities with 10-year plans to end homelessness in Canada and the U.S. (e.g. Calgary, Toronto, Minneapolis, San Diego, New York) and by mental health service providers seeking housing stability for clients (Newman & Goldman, 2008). \n \nFuelled by some scientific evidence (Atherton & McNaughton Nicholls, 2008), and increasingly made popular by press and housing authorities developing “10 year plans” to eradicate homelessness, housing first has emerged as an increasingly popular approach to addressing homelessness. (The HF approach was embraced by all levels of government in Canada, as evidenced by the Streets to Homes initiative in Toronto and the housing initiatives in Calgary). Despite the rapid uptake of this approach, there is the absence of “best practice” evidence to support this. “Best practice” is commonly understood to imply evidence-based techniques or interventions that have been demonstrated to work well with most persons and have the least potential for adverse results. To the extent that there was some, but not conclusive, evidence that HF was effective for those with mental illness and co-occurring mental illness, the Mental Health Commission of Canada (Mental Health Commission of Canada, 2010), introduced a large, multi-site study of HF in five Canadian cities (referred to as the At Home/Chez Soi project). This project is examining the approach in various political contexts and with differing target populations, thereby including the multi-cultural dimensions essential to Canadian adoption of this approach. Although early results are promising, conclusive answers will not be available for several years. In the interim, adoption of the HF approach is rapidly growing.
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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 | 0.167 | 0.600 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.026 | 0.044 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.024 | 0.024 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".