Does flattery work? A comparison of 2 different cover letters for an international survey of orthopedic surgeons.
Bibliographic record
Abstract
BACKGROUND: Surveys are an important tool for gaining information about physicians' beliefs, practice patterns and knowledge. However, the validity of surveys among physicians is often threatened by low response rates. We investigated whether response rates to an international survey could be increased using a more personalized cover letter. METHODS: We conducted an international survey of the 442 surgeon-members of the Orthopaedic Trauma Association on the treatment of femoral-neck fractures. We used previous literature, key informants and focus groups in developing the self-administered 8-page questionnaire. Half of the participants received the survey by mail, and half received an e-mail invitation to participate on the Internet. We alternately allocated participants to receive a "standard" or "test" cover letter. RESULTS: We found a higher primary response rate to the test cover letter (47%) than to the standard cover letter (30%) among those who received the questionnaire by mail. There was no difference between the response rates to the test and to the standard cover letters in the Internet group (22% v. 23%). Overall, there was a higher primary response rate for the test cover letter (34%) when both the mail and Internet groups were combined, compared with the standard cover letter (27%). CONCLUSIONS: Our test cover letter to surgeons in our survey resulted in a significantly higher primary response rate than a standard cover letter when the survey was sent by mail. Researchers should consider using a more personalized cover letter with a postal survey to increase 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 | 0.091 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".