A Population‐Based Evaluation of a Regional Asthma Education Centre
Bibliographic record
Abstract
BACKGROUND: A population-based, ecological evaluation was conducted to determine the impact of a regional asthma education centre on reducing asthma-related morbidity and improving the quality of prescribing. METHODS: The number of emergency department (ED) visits for respiratory-related illness and the prescribing of antiasthmatic medications were monitored during consecutive 18-month pre- and postintervention periods in two communities with similar health care resources. Using defined daily doses, the quality of prescribing was assessed by calculating the ratio of inhaled corticosteroids to inhaled, short-acting beta 2-agonists. RESULTS: The reduction in the rate of respiratory-related ED visits in subjects five to 45 years of age was 410 per 10,000 people and 450 per 10,000 people for the intervention and nonintervention communities, respectively. A significant reduction in the rate of ED visits of 698 per 10,000 people was found for patients aged 35 to 45 years in the intervention community (P<0.05). The reduction achieved statistical significance in the nonintervention community in younger patients: 557 per 10,000 people and 567 per 10,000 people for patients aged five to 14 years and 15 to 24 years, respectively (P<0.05). The ratio of inhaled corticosteroids to inhaled beta 2-agonists increased from 0.47 to 0.78 in the intervention community--a 66% change. However, over the course of the preintervention period, the prescribing ratio was already increasing in this community. The corresponding ratios were 0.47 and 0.53 in the nonintervention community--an increase of 13%. CONCLUSIONS: A conclusive association between the establishment of an asthma education centre and changes in health care use or the quality of prescribing could not be demonstrated.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".