Assessing a community-based asthma education intervention
Bibliographic record
Abstract
Asthma prevalence has increased dramatically in western countries in the last 25 years, and it has been estimated that allergies and asthma affect 30 to 35% of the Canadian population. It has been generally estimated that there are approximately 714,000 Canadians diagnosed with chronic obstructive pulmonary disease (COPD), but it is also estimated that 50% of affected individuals remain undiagnosed, suggesting that there are over 1.4 million Canadians suffering from COPD. The literature has shown that asthma, associated allergies and COPD present tremendous social and health impacts for both individuals and communities; however, a recently piloted community-based asthma education program underscores how difficult it is to increase patient awareness and knowledge about asthma, associated allergies and COPD, improve early detection of these diseases, and therefore improve quality of life and overall chronic disease management. Quantitative surveys, along with asthma, allergy and COPD assessments (peak flow testing) were undertaken to ascertain participants' levels of asthma/COPD, knowledge of these diseases, and disease management strategies. Additional findings from a follow-up survey helped to confirm these findings. Overall, preliminary findings reveal that the pilot's success was limited. Barriers and facilitators to community-based asthma, allergy and COPD education and awareness, peak flow screening, and strategies to encourage proper chronic disease prevention and management, are discussed in this presentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".