Development of a Web‐Based, Work‐Related Asthma Educational Tool for Patients with Asthma
Bibliographic record
Abstract
BACKGROUND: Asthma is a common chronic condition. Work-related asthma (WRA) has a large socioeconomic impact and is increasing in prevalence but remains under-recognized. Although international guidelines recommend patient education, no widely available educational tool exists. OBJECTIVE: To develop a WRA educational website for adults with asthma. METHODS: An evidence-based database for website content was developed, which applied evidence-based website design principles to create a website prototype. This was subsequently tested and serially revised according to patient feedback in three moderated phases (one focus group and two interview phases), followed by face validation by asthma educators. RESULTS: Patients (n=10) were 20 to 28 years of age; seven (70%) were female, three (30%) were in university, two (20%) were in college and five (50%) were currently employed. Key format preferences included: well-spaced, bulleted text; movies (as opposed to animations); photos (as opposed to cartoons); an explicit listing of website aims on the home page; and an exploding tab structure. Participants disliked integrated games and knowledge quizzes. Desired informational content included a list of triggers, prevention⁄control methods, currently available tools and resources, a self-test for WRA, real-life scenario presentations, compensation information, information for colleagues on how to react during an asthma attack and a WRA discussion forum. CONCLUSIONS: The website met the perceived needs of young asthmatic patients. This resource could be disseminated widely and should be tested for its effects on patient behaviour, including job choice, workplace irritant⁄allergen avoidance and⁄or protective equipment, asthma medication use and physician prompting for management of WRA symptoms.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".