Effect of hand versus electronic signatures on response rates in postal surveys: a randomised controlled trial among emergency physicians in Canada
Bibliographic record
Abstract
Objectives Hand signatures offer a more authentic personalisation, which carries over to a sense of trust, although are costly and time-consuming when considering large postal surveys. The objective of this study was to compare response rates when using either hand-signed or electronic-signed letters in a postal survey. Design and setting We embedded this randomised controlled trial within a national cross-sectional postal survey of emergency physicians in Canada. The survey aimed to describe current practice patterns with respect to primary headache disorders. Participants We randomly sampled 500 emergency physicians listed in the Scott’s Canadian Medical Directory, 2019 edition. Interventions Using computer-generated random numbers, physicians were allocated to receiving either hand-signed (n=250) or electronic signed (n=250) letters. The initial mailout contained a US$5 Tim Hortons coffee card with the invitation letter. Four reminders were sent to non-responders every 3 weeks. The same type of signature was used for the initial invitation and subsequent reminders. Outcome The primary outcome was the survey response rate. Results Among 500 physicians invited, 32 invitations were undeliverable. Among the remaining 468 physicians, 231 had been allocated to the hand-signed group and 237 to the electronic signed group. The response rate in the hand-signed group was 87 (37.7%) vs 97 (40.9%) in the electronic-signed group (absolute difference in proportions −3.3%, 95% CI −12.1% to 5.6%). Conclusion There was no significant difference in physician response rate between hand-signed and e-signed cover letter and reminder letters. Electronic signatures should be used in future postal surveys among physicians to save on time and labour without impacting 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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".