How to obtain excellent response rates when surveying physicians
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
This paper outlines ways to maximize response rates to surveys by summarizing the most relevant literature to date and demonstrating how these techniques have resulted in consistently high rates of return in family practice research. We describe the methodology used in recent surveys of physicians conducted by the Centre for Studies in Family Medicine through its Thames Valley Family Practice Research Unit, located in London, Ontario, Canada and funded by the Ontario Ministry of Health and Long-Term Care. The identification and implementation of these techniques to maximize response rates is critical, as primary health care researchers often rely on information gathered through questionnaires to study physicians' practice profiles, experiences and attitudes. Four separate and distinct mailed surveys of physicians using a modified Dillman approach were conducted from 2001 to 2004. The sampling strategies, topics, types of questions and response formats of these surveys varied. The first survey did not use any incentives or recorded delivery/registered mail and received a response rate of 48%. In sharp contrast, the other three surveys obtained responses rates of 76%, 74%, 74%, respectively, achieved through the use of gift certificates and recorded delivery/registered mail. Sending a survey by recorded delivery/registered mail tends to result in the survey package being given priority in the physicians' incoming mail at the practice. Gift certificates partially compensate physicians for time spent completing the survey and recognition of the time required is appreciated. The response rates achieved provide strong evidence to support the use of monetary incentives and recorded delivery/registered mail (along with the Dillman approach) in survey research. It is anticipated that this evidence will be used by other researchers to justify requests for funding to cover the costs associated with incentives and recorded delivery/registered mail. We recommend the use of these strategies to maximize response rates and improve the quality of this type of primary health care research.
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 | 0.078 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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