Measuring patient satisfaction with mental health services in correctional settings: a systematic scoping review
Bibliographic record
Abstract
Introduction: The measurement of patient satisfaction with mental health services is well-established and a key indicator of performance. Patient satisfaction with mental health services received in criminal justice settings however is however less frequently studied. Our aim was to establish how frequently patient satisfaction with mental health services in correctional (prison) settings is being reported, and to identify methods of measurement including all tools that have been used to measure patient satisfaction in these settings. Methods: A comprehensive search of published articles and thesis dissertations was undertaken using multiple databases. Two reviewers independently screened the references to determine eligibility and then extracted the necessary data using a predefined extraction template. Only studies that measured patient satisfaction with a mental health service or intervention within a correctional facility were included. Results: 46 studies, which included various measures, were identified as being eligible for inclusion. The median number of patients involved in these studies was 37.5 (range: 4-1150). Tools were heterogeneous in length, purpose, and design, and these measured a variety of different domains. Most of the tools used had been developed in non-correctional settings and applied in correctional settings without adaptation. Tools with established psychometric properties were used only in ten instances, whereas the majority of the studies reported using author-developed interviews and questionnaires to obtain feedback. Conclusion: Patient satisfaction measurement tools in correctional services are heterogeneous and largely unvalidated; there is no uniformity in the measurement methods used. Systematic Review Registration: https://osf.io/md8vp, identifier md8vp.
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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 | 0.032 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".