IMP2ART systematic review of education for healthcare professionals implementing supported self-management for asthma
Bibliographic record
Abstract
Despite a robust evidence base for its effectiveness, implementation of supported self-management for asthma is suboptimal. Professional education is an implementation strategy with proven effectiveness, though the specific features linked with effectiveness are often unclear. We performed a systematic review of randomised controlled trials and controlled clinical trials (published from 1990 and updated to May 2017 using forward citation searching) to determine the effectiveness of professional education on asthma self-management support and identify features of effective initiatives. Primary outcomes reflected professional behaviour change (provision of asthma action plans) and patient outcomes (asthma control; unscheduled care). Data were coded using the Effective Practice and Organisation of Care Taxonomy, the Theoretical Domains Framework (TDF), and Bloom's Taxonomy and synthesised narratively. Of 15,637 articles identified, 18 (reporting 15 studies including 21 educational initiatives) met inclusion criteria. Risk of bias was high for five studies, and unclear for 10. Three of 6 initiatives improved action plan provision; 1/2 improved asthma control; and 2/7 reduced unscheduled care. Compared to ineffective initiatives, effective initiatives were more often coded as being guideline-based; involving local opinion leaders; including inter-professional education; and addressing the TDF domains 'social influences'; 'environmental context and resources'; 'behavioural regulation'; 'beliefs about consequences'; and 'social/professional role and identity'. Findings should be interpreted cautiously as many strategies were specified infrequently. However, identified features warrant further investigation as part of implementation strategies aiming to improve the provision of supported self-management for asthma.
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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 | 0.018 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".