P4–171: Targeting general practitioners in postal survey research on Alzheimer's disease
Bibliographic record
Abstract
Postal surveys are a useful tool for obtaining information from physicians on the treatment of Alzheimer's disease. To minimize non–response and reduce the time and cost of survey administration, it is important to target physicians who are most likely to treat Alzheimer's disease. Identifying such physicians can pose a particular problem in the case of general practitioners, who treat a wide scope of patients and illnesses. To investigate whether general practitioners who attended continuing medical education courses on geriatrics and the elderly were more likely to respond to a postal survey on drug treatments for Alzheimer's disease than general practitioners who were randomly selected from a master list of physicians. The postal survey was sent to 486 general practitioners in Québec, Canada: 192 were identified a priori as having attended continuing medical education courses on geriatrics and the elderly, while the remaining 294 were randomly selected from the entire pool of Québec general practitioners (n = 7,923) who did not attend the courses. A multiple logistic regression analysis was undertaken to examine whether course attendance was associated with responding to the survey. The analysis was adjusted for three covariates that had data available for all 486 general practitioners: sex, practice location (urban/rural), and language (English/French). General practitioners who attended the continuing medical education courses were more likely to respond to the survey than general practitioners who were selected randomly (response rates = 39% and 18%, respectively [p < 0.0001]). The adjusted odds ratio for responding, after having attended the courses versus being randomly selected, was 2.80 (95% confidence interval = 1.82 to 4.31). To increase response rates among general practitioners, researchers who use postal surveys in Alzheimer's disease research should target general practitioners who have attended continuing medical education courses on geriatrics and the elderly.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".