Survey Design in Orthopaedic Surgery: Getting Surgeons to Respond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.289 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it