Development of intervention-related quality indicators for renal clinical pharmacists using a modified Delphi approach
Bibliographic record
Abstract
OBJECTIVE: To develop a list of renal Quality Indicator Drug therapy problems (QI-DTPs) that serve to advance renal pharmacy practice to improve patient care. METHODS: Eighteen (18) renal, clinical pharmacists participated in an internet-based three-round modified Delphi survey. Each of the three rounds took approximately 2 weeks to complete. Panellists rated 30-candidate renal QI-DTPs using seven selection criteria and one overall consensus criterion on a nine-point Likert scale. Consensus was reached if 75% or more of panellists assigned a score of 7-9 on the consensus criterion during the third Delphi round. KEY FINDINGS: All panellists completed three rounds of Delphi survey. Seventeen-candidate renal QI-DTPs met the consensus definition. CONCLUSIONS: A Delphi panel of renal clinical pharmacists successfully identified 17 consensus renal QI-DTPs. Assessment and implementation of these QI-DTPs will serve to advance renal pharmacy practice and improve patient care.
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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 | 0.116 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".