Facilitators and Solutions for Practicing Optimal Guided Asthma Self‐Management: The Physician Perspective
Bibliographic record
Abstract
OBJECTIVE: To identify key solutions that facilitate the prescription of long-term asthma controller and provision of written self-management plans by physicians. METHODS: One hour individualized semistructured interviews were conducted with physicians. Interviews were transcribed verbatim and analyzed independently by two trained qualitative researchers. A taxonomy of facilitators (contemplated solutions) and experienced solutions was achieved by consensus within the research team. RESULTS: Forty-two physicians (family physicians, pediatricians, emergency physicians, pulmonologists and allergists) were interviewed. The 867 facilitators and solutions, grouped in 10 categories, addressed three physician needs: support physicians in delivering optimal care (guideline dissemination, workplace culture, physician training and experience, physician attitudes toward optimal practice, tools and resources supporting physicians' decision making); assist patients with following recommendations (patient characteristics, experiences and attitudes; physician behaviour; and tools and resources supporting patient self-management); and offer efficient services (reorganization of care; interprofessional patient management). Suggestions pertaining to the latter two categories were most frequently cited to optimize asthma management and use of self-management plans (e.g., access to self-management plans; education by allied health care professionals). The most cited suggestions to support prescribing long-term controller pertained to physician behaviour (e.g., involvement in patient education, personalization of prescriptions, feedback to patients of the benefits of long-term controller). The distribution of facilitators and solutions varied across specialties. CONCLUSIONS: Physicians proposed multiple facilitators and solutions to support optimal practice, leading to the development of a novel taxonomy. Key suggestions varied across physician specialties and behaviours sought, emphasizing the need to carefully select the most promising knowledge translation interventions.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".