Understanding Systems Change in Early Implementation of Housing First in Canadian Communities: An Examination of Facilitators/Barriers, Training/Technical Assistance, and Points of Leverage
Bibliographic record
Abstract
We present interim findings of a cross-site case study of an initiative to expand Housing First (HF) in Canada through training and technical assistance (TTA). HF is an evidence-based practice designed to end chronic homelessness for consumers of mental health services. We draw upon concepts from implementation science and systems change theory to examine how early implementation occurs within a system. Case studies examining HF early implementation were conducted in six Canadian communities receiving HF TTA. The primary data are field notes gathered over 1.5 years and evaluations from site-specific training events (k = 5, n = 302) and regional network training events (k = 4, n = 276). We report findings related to: (a) the facilitators of and barriers to early implementation, (b) the influence of TTA on early implementation, and (c) the "levers" used to facilitate broader systems change. Systems change theory enabled us to understand how various "levers" created opportunities for change within the communities, including establishing system boundaries, understanding how systems components can function as causes of or solutions to a problem, and assessing and changing systems interactions. We conclude by arguing that systems theory adds value to existing implementation science frameworks and can be helpful in future research on the implementation of evidence-based practices such as HF which is a complex community intervention. Implications for community psychology are discussed.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".