A program implementation fidelity assessment of a Housing First program in Ontario
Bibliographic record
Abstract
This research sought to assess the degree of fidelity to the Housing First model achieved by a new Housing First program in a mid-sized Canadian municipal region, and the factors that promoted or hindered fidelity therein. The program was delivering an adaptation to the Housing First model that prioritized home-based support, which was assessed simultaneously. Fidelity ratings were gathered by a team of researchers during a site visit that included observation of a staff meeting, seven interviews with program leaders and staff, two focus groups with program participants, and 10 chart reviews. Overall, the findings show a high degree of fidelity with an average score of 3.55 on a 4-point scale, across 44 fidelity domain items. Results revealed high fidelity in the domains for service philosophy, separation of housing and services and the newly created domain of support and skills development used to assess the home-based support adaptation. Lower scores were found for housing choice and structure, service array, and program design. Challenges to program fidelity were found in housing availability and affordability, service continuation through housing loss, linking with employment and educational services, 24-hour coverage, and participant representation in the program. Factors that could account for these challenges include the low vacancy rates in the jurisdiction, prescriptive policy frameworks, and a slower pace of implementation than anticipated. This study demonstrates the use of a fidelity assessment to provide direct, actionable feedback for program improvement.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".