Quality Indicators for Older Persons’ Transitions in Care: A Systematic Review and Delphi Process
Bibliographic record
Abstract
We identified quality indicators (QIs) for care during transitions of older persons (≥ 65 years of age). Through systematic literature review, we catalogued QIs related to older persons' transitions in care among continuing care settings and between continuing care and acute care settings and back. Through two Delphi survey rounds, experts ranked relevance, feasibility, and scientific soundness of QIs. A steering committee reviewed QIs for their feasible capture in Canadian administrative databases. Our search yielded 326 QIs from 53 sources. A final set of 38 feasible indicators to measure in current practice was included. The highest proportions of indicators were for the emergency department (47%) and the Institute of Medicine (IOM) quality domain of effectiveness (39.5%). Most feasible indicators were outcome indicators. Our work highlights a lack of standardized transition QI development in practice, and the limitations of current free-text documentation systems in capturing relevant and consistent data.
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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 | 0.423 | 0.423 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.045 | 0.028 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".