Getting a Valid Survey Response From 662 Plastic Surgeons in the 21st Century
Bibliographic record
Abstract
INTRODUCTION: Web-based surveys save time and money. As electronic questionnaires have increased in popularity, telephone and mailed surveys have declined. With any survey, a response rate of 75% or greater is critical for the validity of any study. We wanted to determine which survey method achieved the highest response among academic plastic surgeons. METHODS: All American Association of Plastic Surgeons members were surveyed regarding authorship issues. They were randomly assigned to receive the questionnaire through 1 of 4 methods: (A) emailed with a link to an online survey; (B) regular mail; (C) regular mail + $1 bill, and (D) regular mail + $5 bill. Two weeks after the initial mailing, the number of responses was collected, and nonresponders were contacted to remind them to participate. The study was closed after 10 weeks. Survey costs were calculated based on the actual cost of sending the initial survey, including stationary, printing, postage (groups B-D), labor, and cost of any financial incentives. Cost of reminders to nonresponders was calculated at $5 per reminder, giving a total survey cost. RESULTS: Of 662 surveys sent, 54 were returned because of incorrect address/email, retirement, or death. Four hundred seventeen of the remaining 608 surveys were returned and analyzed. The response rate was lowest in the online group and highest in those mailed with a monetary incentive. CONCLUSIONS: Despite the convenience and low initial cost of web-based surveys, this generated the lowest response. We obtained statistically significant response rates (79% and 84%) only by using postal mail with monetary incentives and reminders. The inclusion of a $1 bill represented the greatest value and cost-effective survey method, based on cost per response.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".