Do School-Based Asthma Education Programs Improve Self-Management and Health Outcomes?
Bibliographic record
Abstract
CONTEXT: Asthma self-management education is critical for high-quality asthma care for children. A number of studies have assessed the effectiveness of providing asthma education in schools to augment education provided by primary care providers. OBJECTIVE: To conduct a systematic review of the literature on school-based asthma education programs. METHODS: As our data sources, we used 3 databases that index peer-reviewed literature: MEDLINE, the Cochrane Central Register of Controlled Trials, and the Cumulative Index to Nursing and Allied Health Literature. Inclusion criteria included publication in English and enrollment of children aged 4 to 17 years with a clinical diagnosis of asthma or symptoms consistent with asthma. RESULTS: Twenty-five articles met the inclusion criteria. Synthesizing findings across studies was difficult because the characteristics of interventions and target populations varied widely, as did the outcomes assessed. In addition, some studies had major methodologic weaknesses. Most studies that compared asthma education to usual care found that school-based asthma education improved knowledge of asthma (7 of 10 studies), self-efficacy (6 of 8 studies), and self-management behaviors (7 of 8 studies). Fewer studies reported favorable effects on quality of life (4 of 8 studies), days of symptoms (5 of 11 studies), nights with symptoms (2 of 4 studies), and school absences (5 of 17 studies). CONCLUSIONS: Although findings regarding effects of school-based asthma education programs on quality of life, school absences, and days and nights with symptoms were not consistent, our analyses suggest that school-based asthma education improves knowledge of asthma, self-efficacy, and self-management behaviors.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".