De-Implementation: Lessons to be Learned when Abandoning Inappropriate Homelessness Interventions
Bibliographic record
Abstract
Evidence on what works to end homelessness is growing. Evidence also highlights that some forms of help are harmful and should be de-implemented. The ability to abandon low-functioning interventions is considered essential to improve conditions for homeless people. It is common for challenges to be encountered when evidence exists claiming that alternative approaches are more effective and/or cost-effective. This is particularly true in the context of the problematic staircase model and the highly effective Housing First. In this study, the aim was to collect published articles on the process of abandoning established methods with low scientific support. This scoping review explores evidence on de-implementation that may clarify why it can be difficult to introduce interventions like Housing First despite having strong scientific evidence. The call for a shift toward greater provision of Housing First in Sweden underlines the timeliness of this problem. Forty-one articles published between 2014 and 2020 were included. The review found no articles focusing on the de-implementation of homelessness services. Findings from other fields show that the important first step is to identify what needs to be phased out. Together with organized demands from users and favorable financial effects, scientific evidence can constitute driving mechanisms for de-implementation. We found a lack of practical frameworks and theoretical explanations that could support successful phasing out of unnecessary interventions in the homelessness field. It is suggested that to support the implementation of new ways of working that better benefit homeless people, we must pay attention to established ways of working. This requires a developed theory of de-implementation of homelessness interventions and calls for more robust research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".