Optimizing Communication Strategies for COPD Management: Effectiveness of Educational Video and Pamphlet Interventions
Bibliographic record
Abstract
Abstract Objectives Risk prediction models are increasingly used at point of care to support personalized treatment decisions. This study created and evaluated two Information, Education, and Communication (IEC) resources to improve public understanding of a risk prediction tool for Chronic Obstructive Pulmonary Disease (COPD) management. Methods We created a 5-minute video and a pamphlet explaining the burden of COPD and how a prediction model generates quantitative estimates of, and benefit of certain treatments for, exacerbations of the disease. These tools were tested among students and researchers in public health. A patient partner was engaged throughout to ensure the materials were accessible and patient-centered. Results Twenty-five individuals participated (80% female; 60% aged 25–64). After reviewing the materials, 92% of participants agreed to the statement “I am familiar with the idea of precision medicine approach”. Most (72%) felt they received sufficient information about the tool, and 92% believed such materials could support patient decision. Participants stated that the materials were clear, detailed, and written in plain language. Participants preferred the pamphlet (68%) over the video (44%). Suggestions for improvement included expanding content on how the tool works. Conclusions The findings of this study provided a better understanding of how to present complex medical information around precision medicine that is accessible and meaningful to diverse audiences. We will improve our materials based on these comments, and continue to make them available at https://resp.core.ubc.ca/show/patient_committee_2025
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".