A Proposal for a Best-evidence Model of Care and Program Logic for Supported Accommodation for People Released From Prison
Bibliographic record
Abstract
This paper describes the development of a proposed best-evidence model of care (MoC) and program logic (PL) for supported accommodation (SA) for people released from prison. Evidence from a systematic review, interviews with clients of SA, and consultation with service providers were synthesized to develop a draft MoC that was embedded into a PL. The MoC and PL were refined in a workshop with researchers and SA providers. The MoC comprised five best-evidence core components to be standardized across any SA, operationalized by flexible activities that need to be determined by services to suit their circumstances. The PL comprised client needs that the program targets, a rationale for why core components would be effective and appropriate process and outcome measures. The development and uptake of a best-evidence MoC and clearly defined PL will help engender a larger and more rigorous SA evidence-base, and improve outcomes for people released from prison.
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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 | 0.181 | 0.187 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".