Evidence for Publicly Reported Quality Indicators in Residential Long-Term Care: A Systematic Review
Bibliographic record
Abstract
Abstract Quality indicators (QIs) are used internationally to measure, compare and improve quality in residential long-term care. Public reporting of such indicators allows transparency and motivates local quality improvement initiatives. However, little is known about the quality of QIs. In a systematic literature review, we assessed which countries publicly report health-related QIs, whether stakeholders were involved in their development and the evidence concerning their validity and reliability. Most information was found in grey literature, with nine countries (USA, Canada, Australia, New Zealand and five countries in Europe) publicly reporting a total of 66 QIs in areas like mobility, falls, pressure ulcers, continence, pain, weight loss, and physical restraint. While USA, Canada and New Zealand work with QIs from the Resident Assessment Instrument – Minimal Data Set (RAI-MDS), the other countries developed their own QIs. All countries involved stakeholders in some phase of the QI development. However, we only found reports from Canada and Australia on both, the criteria judged (e.g. relevance, influenceability), and the results of structured stakeholder surveys. Interrater reliability was measured for some RAI QIs and for those used in Germany, showing overall good Kappa values (>0.6) except for QIs concerning mobility, falls and urinary tract infection. Validity measures were only found for RAI QIs and were mostly moderate. Although a number of QIs are publicly reported and used for comparison and policy decisions, available evidence is still limited. We need broader and accessible evidence for a responsible use of QIs in public reporting.
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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 | 0.093 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".