An Evaluation of Housing First Programs in Calgary
Bibliographic record
Abstract
Calgary is one of many cities internationally that has implemented a Housing First (HF) model for reducing homelessness. The HF model is based on the philosophy that stable housing, provided without preconditions, is a necessary prerequisite for helping someone deal with the issues that have caused them to become homeless. The original HF model, New York City’s Pathways Housing First (PHF) model, has been closely studied with the use of controlled experiments involving clients whose personal characteristics match those for whom the model is designed. Based on the favourable evidence drawn from these controlled experiments, many jurisdictions have implemented HF programs. These include many programs that have drifted a considerable distance from the design of the Pathways model. Despite this drift in program design, HF programs continue to find support from governments comforted by the favourable results of controlled experiments. In this paper we evaluate HF programs implemented in a real world, non-experimental setting over many years. Evaluating HF programs as they are implemented in large scale non-experimental settings is important for determining their practical usefulness to system operators and policymakers. Using richly detailed administrative datasets, we evaluate the success of HF programs as implemented in Calgary over the period 2012 to 2018. We identify the extent of the drift of these HF programs from the original Pathways model and show how the personal characteristics of people chosen for HF programs influence the rate of program success. We find that HF programs in Calgary have proven very successful in graduating people to permanent housing and reducing the number of people returning to homelessness. Fifty-five per cent of clients enrolled in HF programs, many of whom are dealing with the debilitating effects if mental health challenges, substance abuse, and prolonged periods of homelessness, remained housed in HF programs or graduate to permanent housing without case management support. We argue that this rate of success is comparable to that claimed for PHF programs evaluated under controlled conditions and for much shorter periods. Although the devil is in his usual place when it comes to evaluating the success of any HF program, our results are suggestive that limited deviations from the Pathways model, such as those we observed in Calgary, do not significantly affect the rates of success reported from controlled experiments.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".