What is the value and impact of the adaptation process on quality indicators for local use? A scoping review
Bibliographic record
Abstract
BACKGROUND: Quality indicators (QIs) are designed for improving quality of care, but the development of QIs is resource intensive and time consuming. OBJECTIVE: To describe and identify the impact and potential attributes of the adaptation process for the local use of existing QIs. DATA SOURCES: EMBASE, MEDLINE, CINAHL and grey literature were searched. STUDY SELECTION: Literatures operationalizing or implementing QIs that were developed in a different jurisdiction from the place where the QIs were included. RESULTS: Of 7704 citations identified, 10 out of 33 articles were included. Our results revealed a lack of definition and conceptualization for an adaptation process in which an existing set of QIs was applied. Four out of ten studies involved a consensus process (e.g., Delphi or RAND process) to determine the suitability of QIs for local use. QIs for chronic conditions in primary and secondary settings were mostly used for adaptation. Of the ones that underwent a consensus process, 56.3 to 85.7% of original QIs were considered valid for local use, and 2 to 21.8% of proposed QIs were newly added. Four attributes should be considered in the adaptation: 1) identifying areas/conditions; 2) a consensus process; 3) proposing adapted QIs; 4) operationalization and evaluation. CONCLUSION: The existing QIs, although serving as a good starting point, were not adequately adapted before for use in a different jurisdiction from their origin. Adaptation of QIs under a systematic approach is critical for informing future research planning for QIs adaptation and potentially establishing a new pathway for healthcare 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 | 0.219 | 0.508 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.031 | 0.039 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".