Using a knowledge translation framework to implement asthma clinical practice guidelines in primary care
Bibliographic record
Abstract
Quality problem International guidelines establish evidence-based standards for asthma care; however, recommendations are often not implemented and many patients do not meet control targets. Initial assessment Regional pilot data demonstrated a knowledge-to-practice gap. Choice of solutions We engineered health system change in a multi-step approach described by the Canadian Institutes of Health Research knowledge translation framework. Implementation Knowledge translation occurred at multiple levels: patient, practice and local health system. A regional administrative infrastructure and inter-disciplinary care teams were developed. The key project deliverable was a guideline-based interdisciplinary asthma management program. Six community organizations, 33 primary care physicians and 519 patients participated. The program operating cost was $290/patient. Evaluation Six guideline-based care elements were implemented, including spirometry measurement, asthma controller therapy, a written self-management action plan and general asthma education, including the inhaler device technique, role of medications and environmental control strategies in 93, 95, 86, 100, 97 and 87% of patients, respectively. Of the total patients 66% were adults, 61% were female, the mean age was 35.7 (SD = ± 24.2) years. At baseline 42% had two or more symptoms beyond acceptable limits vs. 17% (P< 0.001) post-intervention; 71% reported urgent/emergent healthcare visits at baseline (2.94 visits/year) vs. 45% (1.45 visits/year) (P< 0.001); 39% reported absenteeism (5.0 days/year) vs. 19% (3.0 days/year) (P< 0.001). The mean follow-up interval was 22 (SD = ± 7) months. Lessons learned A knowledge-translation framework can guide multi-level organizational change, facilitate asthma guideline implementation, and improve health outcomes in community primary care practices. Program costs are similar to those of diabetes programs. Program savings offset costs in a ratio of 2.1:1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".