Strategies to Balance Fidelity to Housing First Principles with Local Realities: Lessons from a Large Urban Centre
Bibliographic record
Abstract
The importance of program implementation in achieving desired outcomes is well-documented, but there remains a need for concrete guidance on how to achieve fidelity to evidence-based models within dynamic local contexts. Housing First (HF), an evidence-based model for people experiencing homelessness and mental illness, provides an important test-case for such guidance; it targets a uniquely underserved subpopulation with complex needs, and is delivered by practitioners with varying knowledge and skill levels. Scientific evidence affirms HF's effectiveness, but its rapid dissemination has outpaced the ability to monitor not only whether it is being implemented with fidelity, but also how this can be achieved within variable local contexts and challenges. This qualitative study contributes to this need by capturing insights from practitioners on implementation challenges and specific strategies developed to overcome them. Findings reinforce the importance of developing HF-specific implementation guidelines, and of engaging relevant stakeholders throughout all phases of that development.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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".