Improving Conversations With COVID-19 Vaccine Hesitant Patients: Action Research to Support Family Physicians
Bibliographic record
Abstract
Vaccination delivery and efforts to counter vaccine hesitancy have become focal issues for family medicine teams as the COVID-19 pandemic has evolved. Conducting action research, our team developed an interactive web-based guide to improve clinical conversations around a broad range of vaccine hesitancies presented by patients. The paper presents a step-by-step account of the guide being codesigned with family physicians—its targeted end users—in a process that included validation interviews; role-play interviews; and user-tested design. The validation interviews sought to understand the pragmatic realities of vaccine hesitancy in family medicine clinical practice relative to relevant psychological theories. The role-play interviews drew out conversational strategies and advice from family physicians. The principles of motivational interviewing—an evidence-based approach to vaccine hesitancy conversations that supplements information deficit approaches—were used to codesign the content and layout of the guide. User counts, stakeholder engagement, and web-based analytics indicate the guide is being used extensively. Formal evaluation of the guide is presently underway. Originally published as Annals “Online First” article.
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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 | 0.073 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".