A Children’s Asthma Education Program: Roaring Adventures of Puff (RAP), Improves Quality of Life
Bibliographic record
Abstract
BACKGROUND: It is postulated that children with asthma who receive an interactive, comprehensive education program would improve their quality of life, asthma management and asthma control compared with children receiving usual care. OBJECTIVE: To assess the feasibility and impact of 'Roaring Adventures of Puff' (RAP), a six-week childhood asthma education program administered by health professionals in schools. METHODS: Thirty-four schools from three health regions in Alberta were randomly assigned to receive either the RAP asthma program (intervention group) or usual care (control group). Baseline measurements from parent and child were taken before the intervention, and at six and 12 months. RESULTS: The intervention group had more smoke exposure at baseline. Participants lost to follow-up had more asthma symptoms. Improvements were significantly greater in the RAP intervention group from baseline to six months than in the control group in terms of parent's perceived understanding and ability to cope with and control asthma, and overall quality of life (P<0.05). On follow-up, doctor visits were reduced in the control group. CONCLUSION: A multilevel, comprehensive, school-based asthma program is feasible, and modestly improved asthma management and quality of life outcomes. An interactive group education program offered to children with asthma at their school has merit as a practical, cost-effective, peer-supportive approach to improve health outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".