The Importance of Relevance: Willingness to Share eHealth Data for Family Medicine Research
Bibliographic record
Abstract
Objective: To determine the willingness of family medicine patients to allow their eHealth data to be used for research purposes, and evaluate how patient characteristics and the relevance of research impact that decision. Design: Cross-sectional questionnaire. Setting: Acute care respiratory clinic or an outpatient family medicine clinic in Montreal, Quebec. Participants: 474 waiting room patients recruited via convenience sampling. Main Outcome Measures: A self-administered questionnaire collected data on age, gender, employment status, education, mother tongue and perceived health status. The relevance of three research scenarios and willingness to share their anonymized data was evaluated by each respondent. Responses were compared for family practice versus specialty care patients. Willingness to share anonymized data and personal relevance was indicated for each research scenario, refusal to share eHealth data relative to patient characteristics was the main outcome. Results: The questionnaire was completed by 229 family medicine respondents and 245 outpatient respondents. Almost a quarter of all respondents felt the research was not relevant. Family medicine patients (9.2%) were unwilling to allow their data to be used for at least one scenario versus 11.3% in the outpatient clinic. Lack of relevance (OR 11.55; 95% CI 5.12-26.09) and being in family practice (OR 2.13; 95% CI 1.06-4.27) increased the likelihood of refusal to share data for research. Conclusion: Family medicine patients were less willing to share eHealth data, but the refusal rate for both family medicine and outpatients in specialty clinics was high. Personal relevance of the research had a strong impact on the responses arguing for better efforts to make primary care research more pertinent to patients.
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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 | 0.031 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".