Bringing experiences of healthcare in custody into quality improvement
Bibliographic record
Abstract
Patient experience is an essential component of safe and high-quality healthcare, yet rarely examined in the context of carceral settings. This article describes a project undertaken by the Ontario Ministry of the Solicitor General to collect evidence and perspectives on how to bring patient experiences of healthcare services delivered in provincial correctional facilities into ongoing quality improvement work. We first conducted a scoping review and jurisdictional scan to learn from existing processes and experiences. We then engaged frontline healthcare providers delivering services in custody and people with recent experience of incarceration regarding priority measures and processes for data collection and mechanisms for implementing evidence-based change. This article describes methods used to engage stakeholders, including a survey and focus groups, as well as key lessons learned. This work is relevant to readers experiencing barriers to patient engagement, interested in collaborative research processes, and developing services for people who have experienced incarceration.
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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 | 0.044 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".