Evidence-based performance indicators of primary care for asthma: a modified RAND Appropriateness Method
Bibliographic record
Abstract
PURPOSE: To develop evidence-based performance indicators that measure the quality of primary care for asthma. DATA SOURCES: Cochrane Database of Systematic Reviews, MEDLINE, EMBASE and CINAHL for peer-reviewed articles published in 1998-2008 and five national/global asthma management guidelines. STUDY SELECTION: Articles with a focus on current asthma performance indicators recognized or used in community and primary care settings. Data extraction Modified RAND Appropriateness METHOD: was used. The work described herein was conducted in Canada in 2008. Five clinician experts conducted the systematic literature review. Asthma-specific performance indicators were developed and the strength of supporting evidence summarized. A survey was created and mailed to 17 expert panellists of various disciplines, asking them to rate each indicator using a 9-point Likert scale. Percentage distribution of the Likert scores were generated and given to the panellists before a face-to-face meeting, which was held to assess consensus. At the meeting, they ranked all indicators based on their reliability, validity, availability and feasibility. RESULTS: Literature search yielded 1228 articles, of which 135 were used to generate 45 performance indicators in five domains: access to care, clinical effectiveness, patient centeredness, system integration and coordination and patient safety. The top five ranked indicators were: Asthma Education from Certified Asthma Educator, Pulmonary Function Monitoring, Asthma Control Monitoring, Controller Medication Use and Asthma Control. CONCLUSION: The top 15 ranked indicators are recommended for implementation in primary care to measure asthma care delivery, respiratory health outcomes and establish benchmarks for optimal health service delivery over time and across populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.233 | 0.492 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".