Playing Cards on Asthma Management: A New Interactive Method for Knowledge Transfer to Primary Care Physicians
Bibliographic record
Abstract
OBJECTIVES: To describe an interactive playing card workshop in the communication of asthma guidelines recommendations, and to assess the initial evaluation of this educational tool by family physicians. DESIGN: Family physicians were invited to participate in the workshop by advertisements or personal contacts. Each physician completed a standardized questionnaire on his or her perception of the rules, content and properties of the card game. SETTING: A university-based continuing medical education initiative. PARTICIPANTS: Primary care physicians. MAIN OUTCOME MEASURES: Physicians' evaluation of the rules, content and usefulness of the program. RESULTS: The game allowed the communication of relevant asthma-related content, as well as experimentation with a different learning format. It also stimulated interaction in a climate of friendly competition. Participating physicians considered the method to be an innovative tool that facilitated reflection, interaction and learning. It generated relevant discussions on how to apply guideline recommendations to current asthma care. CONCLUSIONS: This new, interactive, educational intervention, integrating play and scientific components, was well received by participants. This method may be of value to help integrate current guidelines into current practice, thus facilitating knowledge transfer to caregivers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".