Developing guideline-based quality indicators
Bibliographic record
Abstract
AIM: In this article, the authors discuss a multiphase approach for developing quality indicators based on pain practice guidelines, and the challenges associated with the process. The presentation is based on previously published reporting standards for guideline-based quality indicators. METHODS: The following steps of the indicator development process were undertaken: topic selection; guideline selection; extraction of recommendations; quality indicator selection and practice test. RESULTS: Eleven practice guidelines were reviewed for quality, and three high-quality guidelines were compared for pertinent recommendations. From these three guidelines, 12 recommendations were extracted and judged appropriate to examine the practice gap for nursing students and clinicians on an oncology and palliative care unit. Quality indicators were then identified by a consensus process, resulting in 24 discrete indicators that were included in the practice test. CONCLUSION: Quality indicators can be used to examine gaps in pain management practice, and to evaluate change after guideline implementation. However, their development can be challenging, and guideline developers could facilitate uptake of guidelines by including clear, relevant quality indicators as part of guideline creation and presentation.
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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 | 0.330 | 0.523 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".