Why are response rates in clinician surveys declining?
Bibliographic record
Abstract
OBJECTIVE: To understand why response rates in clinician surveys are declining. DESIGN: Cross-sectional fax-back survey. SETTING: British Columbia. PARTICIPANTS: Random sample of family physicians and all gynecologists in the College of Physicians and Surgeons of British Columbia's registry. MAIN OUTCOME MEASURES: Accuracy of the College of Physicians and Surgeons of British Columbia's registry, and the prevalence and characteristics of physicians with policies not to participate in any surveys. RESULTS: Of 542 physicians who received surveys, 76 (14.0%) responded. On follow-up we found the following: the College of Physicians and Surgeons of British Columbia's registry was inaccurate for 94 (17.3%) listings; 14 (2.6%) physicians were away; 100 (18.5%) were not eligible; and 197 (36.3%) had an office policy not to participate in any surveys. Compared with the respondents, physicians with an office policy not to participate in any surveys were more likely to be men, less likely to be white, more likely to have urban-based practices, and more likely to have been in practice for more than 15 years. CONCLUSION: Many physicians have an office policy not to participate in any surveys. Owing to the trend of lower response rates, recommendations of minimum response rates for clinician surveys by many journals might need to be reassessed.
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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 | 0.547 | 0.760 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".