Survey Design in Orthopaedic Surgery: Getting Surgeons to Respond
Bibliographic record
Abstract
We provide an overview of survey design and implementation strategies in orthopaedic surgery. Health-care surveys are vital for obtaining information on the beliefs, patterns of practice, attitudes, and behaviors of orthopaedic surgeons. It is important to obtain a high response rate from administered surveys to reduce bias due to nonresponse. Researchers should follow the guidelines provided by this review to increase the response rate of orthopaedic surgeons to surveys. When designing these surveys, the researcher must consider length, format, and aesthetics. In addition, the types of questions that are included, the wording of these questions, and the order in which the questions are presented within the survey need to be carefully considered. Surveys can be administered by telephone, mail, facsimile (fax), and electronically by e-mail or Internet. The use of a mixed-mode method is recommended to improve the response rate. To increase the response rate to surveys that are directed at health professionals, a number of strategies have been suggested, including using cover letters, personalizing the cover letter and survey package, pretesting the cover letter and survey, contacting the surgeons prior to administration of the survey, contacting the surgeons multiple times, using stamped return envelopes in mail surveys, using appropriate survey packaging styles, providing incentives, and ensuring that the orthopaedic surgeon recognizes the sender of the survey. The costs associated with each administration method are briefly discussed, and ethical considerations are reviewed.
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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 | 0.221 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".