The Effects of Tracking Responses and the Day of Mailing on Physician Survey Response Rate: Three Randomized Trials
Bibliographic record
Abstract
BACKGROUND: The response rates to physician postal surveys remain modest. The primary objective of this study was to assess the effect of tracking responses on physician survey response rate (i.e., determining whether each potential participant has responded or not). A secondary objective was to assess the effects of day of mailing (Monday vs. Friday) on physician survey response rate. METHODS: We conducted 3 randomized controlled trials. The first 2 trials had a 2 x 2 factorial design and tested the effect of day of mailing (Monday vs. Friday) and of tracking vs. no tracking responses. The third trial tested the effect of day of mailing (Monday vs. Friday). We meta-analyzed these 3 trials using a random effects model. RESULTS: The total number of participants in the 3 trials was 1339. The response rate with tracked mailing was not statistically different from that with non-tracked mailing by the time of the first reminder (RR = 1.01 95% CI 0.84, 1.22; I² = 0%). There was a trend towards lower response rate with tracked mailing by the time of the second reminder (RR = 0.91; 95% CI 0.78, 1.06; I² = 0%). The response rate with mailing on Mondays was not statistically different from that with Friday mailing by the time of first reminder (RR = 1.01; 95% CI 0.87, 1.17; I² = 0%), and by the time of the 2(nd) reminder (RR = 1.08; 95% CI 0.84, 1.39; I² = 77%). CONCLUSIONS: Tracking response may negatively affect physicians' response rate. The day of mailing does not appear to affect physicians' response rate.
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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 | 0.039 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".