Towards Excellence in Asthma Management: Final Report of an Eight‐Year Program Aimed at Reducing Care Gaps in Asthma Management in Quebec
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Asthma care in Canada and around the world persistently falls short of optimal treatment. To optimize care, a systematic approach to identifying such shortfalls or 'care gaps', in which all stakeholders of the health care system (including patients) are involved, was proposed. METHODS: Several projects of a multipartner, multidisciplinary disease management program, developed to optimize asthma care in Quebec, was conducted in a period of eight years. First, two population maps were produced to identify regional variations in asthma-related morbidity and to prioritize interventions for improving treatment. Second, current care was evaluated in a physician-patient cohort, confirming the many care gaps in asthma management. Third, two series of peer-reviewed outcome studies, targeting high-risk populations and specific asthma care gaps, were conducted. Finally, a process to integrate the best interventions into the health care system and an agenda for further research on optimal asthma management were proposed. RESULTS: Key observations from these studies included the identification of specific patterns of noncompliance in using inhaled corticosteroids, the failure of increased access to spirometry in asthma education centres to increase the number of education referrals, the transient improvement in educational abilities of nurses involved with an asthma hotline telephone service, and the beneficial effects of practice tools aimed at facilitating the assessment of asthma control and treatment needs by general practitioners. CONCLUSIONS: Disease management programs such as Towards Excellence in Asthma Management can provide valuable information on optimal strategies for improving treatment of asthma and other chronic diseases by identifying care gaps, improving guidelines implementation and optimizing 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".