National survey of physicians to determine the effect of unconditional incentives on response rates of physician postal surveys
Bibliographic record
Abstract
OBJECTIVES: Physicians are a commonly targeted group in health research surveys, but their response rates are often relatively low. The goal of this paper was to evaluate the effect of unconditional incentives in the form of a coffee card on physician postal survey response rates. DESIGN: Following 13 key informant interviews and eight cognitive interviews a survey questionnaire was developed. PARTICIPANTS: A random sample of 534 physicians, stratified by physician group (geriatricians, family physicians, emergency physicians) was selected from a national medical directory. SETTING: Using computer generated random numbers; half of the physicians in each stratum were allocated to receive a coffee card to a popular national coffee chain together with the first survey mailout. INTERVENTIONS: The intervention was a $10 Tim Hortons gift card given to half of the physicians who were randomly allocated to receive the incentive. RESULTS: 265 (57.0%) physicians completed the survey. The response rate was significantly higher in the group allocated to receive the incentive (62.7% vs 51.3% in the control group; p=0.01). CONCLUSIONS: Our results indicate that an unconditional incentive in the form of a coffee gift card can substantially improve physician response rates. Future research can look at the effect of varying amounts of cash on the gift cards on response rates.
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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 | 0.121 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".