Using the theoretical domains framework to identify barriers and enablers to pediatric asthma management in primary care settings
Bibliographic record
Abstract
Objectives: This study aimed to apply a theory-based approach to identify barriers and enablers to implementing the Alberta Primary Care Asthma Pediatric Pathway (PCAPP) into clinical practice. Phase 1 included an assessment of assumptions underlying the intervention from the perspectives of the developers. Phase 2 determined the perceived barriers and enablers for: 1) primary care physicians' prescribing practices, 2) allied health care professionals' provision of asthma education to parents, and 3) children and parents' adherence to their treatment plans. Methods: Interviews were conducted with 35 individuals who reside in Alberta, Canada. Phase 1 included three developers. Phase 2 included 11 primary care physicians, 10 allied health care professionals, and 11 parents of children with asthma. Phase 2 interviews were based on the 14 domains of the Theoretical Domains Framework (TDF). Transcribed interviews were analyzed using a directed content analysis. Key assumptions by the developers about the intervention, and beliefs by others about the barriers and enablers of the targeted behaviors were identified. Results: Eight TDF domains mapped onto the assumptions of the pathway as described by the intervention developers. Interviews with health care professionals and parents identified nine TDF domains that influenced the targeted behaviors: knowledge, skills, beliefs about capabilities, social/professional role and identity, beliefs about consequences, environmental context and resources, behavioral regulation, social influences, and emotions. Conclusions: Barriers and enablers perceived by health care professionals and parents that influenced asthma management will inform the optimization of the PCAPP prior to its evaluation.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".