Assessing a Population-Based Approach to Asthma and COPD Education
Bibliographic record
Abstract
In 2008, chronic respiratory disease—including asthma and chronic obstructive pulmonary disease (COPD)—was the fourth leading cause of death in Canada. Chronic respiratory disease represents a major burden of illness on individuals, families, and the health system. Provision of asthma and COPD education and management resources can serve to improve self-management strategies, leading to an improved quality of life. The Partnership in Lung Age Testing and Education (PLATE) program was a unique demonstration project examining a population-based approach to the management of respiratory disease. Objectives included improving patient education, increasing public awareness about chronic respiratory disease, and promoting a healthy lifestyle. First, 13 “Airways Clinics” in Toronto and Hamilton were undertaken in various community settings (eg, pharmacies, shopping malls), providing respiratory health education and FEV1 measurement. Second, a follow-up survey examined knowledge gained from phase I. Eighty-seven participants included those with physician-diagnosed asthma, physician-diagnosed COPD, and symptomatic without diagnosis and/or long-time smokers. Airways Clinics were positively received by participants, with 77% of respondents more aware of the role healthy lifestyles play in disease management. Findings indicate that the highest level of interest came from high-needs communities, including low-income and older populations (between 50 and 79 years).
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".