Quality indicators for schizophrenia care: A scoping review
Bibliographic record
Abstract
Measuring quality of care is a critical first step towards improving the healthcare contributing to persistent poor outcomes experienced by many people living with schizophrenia. This scoping review aims to identify and characterize indicators for measuring the quality of care for people living with schizophrenia. We searched 6 academic databases, 4 grey literature databases, and 23 organization websites for documents containing quality indicators developed for or applied in a population with schizophrenia-spectrum disorders. We identified 119 unique documents, yielding 390 distinct quality indicators. Most measures were process indicators (68 %; n = 267) commonly reflecting safety (30 %; n = 118) and effectiveness (35 %; n = 136) domains of quality of care. Quality indicators included measures of primarily mental healthcare (77 %; n = 299), as well as physical healthcare (23 %; n = 91). Indicators reflected aspects of care related to service delivery, pharmacotherapy, assessments, resources and policies, psychological interventions, social and other interventions. Indicator development was notable for a lack of well-described validation and selection processes. Gaps in indicator availability for comorbid substance use, reproductive health, and healthcare equity were also identified. Results reflect a growing recognition of the importance of quality measurement in this population but highlight the need for prioritization of indicators to guide future quality measurement and improvement.
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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 | 0.028 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.033 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".