Response rates in postal surveys of healthcare professionals between 1996 and 2005: An observational study
Bibliographic record
Abstract
BACKGROUND: Postal surveys are a frequently used method of data collection in health services research. Low response rates increase the potential for bias and threaten study validity. The objectives of this study were to estimate current response rates, to assess whether response rates are falling, to explore factors that might enhance response rates and to examine the potential for non-response bias in surveys mailed to healthcare professionals. METHODS: A random sample of postal or electronic surveys of healthcare workers (1996-2005) was identified from Medline, Embase or Psycinfo databases or Biomed Central. Outcome measures were survey response rate and non response analysis. Multilevel, multivariable logistic regression examined the relationship between response rate and publication type, healthcare profession, country and number of survey participants, questionnaire length and use of reminders. RESULTS: The analysis included 350 studies. Average response rate in doctors was 57.5% (95%CI: 55.2% to 59.8%) and significantly lower than the estimate for the prior 10 year period. Response rates were higher when reminders were sent (adjusted OR 1.3; 95%CI 1.1-1.6) but only half the studies did this. Response rates were also higher in studies with fewer than 1000 participants and in countries other than US, Canada, Australia and New Zealand. They were not significantly affected by publication type or healthcare profession (p > 0.05). Only 17% of studies attempted assessment of possible non-response bias. CONCLUSION: Response rates to postal surveys of healthcare professionals are low and probably declining, almost certainly leading to unknown levels of bias. To improve the informativeness of postal survey findings, researchers should routinely consider the use of reminders and assess potential for non-response bias.
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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 | 0.038 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".