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 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 | 0.095 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".