Response rates for surveys of chiropractors
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
BACKGROUND: Survey response rates may vary by type of practitioner studied and may have declined over time. Response rates for surveys of complementary practitioners have not been studied. OBJECTIVE: To describe the response rates in published surveys of chiropractors and explore for secular trends in response rates and for methodologic and geographic correlates of response rates. METHODS: Secondary analysis of data extracted from published English language reports of surveys of chiropractors. Response rates were calculated as the total number of persons from whom a questionnaire was returned divided by the total number of persons who were sent a questionnaire. RESULTS: Sixty-two surveys represented by 79 articles published in the interval 1980 to 2000 met inclusion criteria for analysis. We were able to calculate a response rate for 46 postal surveys. The mean response rate was 52.7%. There was no significant association between geographic setting and response rate, and there was no evidence of secular trend in response rates. None of the studies employed incentives. The strongest predictor of response rate was number of contacts with the target population. CONCLUSION: Response rates for surveys of chiropractors are similar to those observed for surveys of medical doctors. The key to obtaining high response rates is the use of evidence-based methods in design and conduct of the surveys.
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 | 0.025 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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