Effect of numbering of return envelopes on participation, explicit refusals, and bias: experiment and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Tracing mail survey responses is useful for the management of reminders but may cause concerns about anonymity among prospective participants. We examined the impact of numbering return envelopes on the participation and the results of a survey on a sensitive topic among hospital staff. METHODS: In a survey about regrets associated with providing healthcare conducted among hospital-based doctors and nurses, two randomly drawn subsamples were provided numbered (N = 1100) and non-numbered (N = 500) envelopes for the return of completed questionnaires. Participation, explicit refusals, and item responses were compared. We also conducted a meta-analysis of the effect of questionnaire/envelope numbering on participation in health surveys. RESULTS: The participation rate was lower in the "numbered" group than in the "non-numbered" group (30.3% vs. 35.0%, p = 0.073), the proportion of explicit refusals was higher in the "numbered" group (23.1% vs 17.5%, p = 0.016), and the proportion of those who never returned the questionnaire was similar (46.6% vs 47.5%, p = 0.78). The means of responses differed significantly for 12 of 105 items (11.4%), which did not differ significantly from the expected frequency of type 1 errors, i.e., 5% (permutation test, p = 0.078). The meta-analysis of 7 experimental surveys (including this one) indicated that numbering is associated with a 2.4% decrease in the survey response rate (95% confidence interval 0.3% to 4.4%). CONCLUSIONS: Numbered return envelopes may reduce the response rate and increase explicit refusals to participate in a sensitive survey. Reduced participation was confirmed by a meta-analysis of randomized health surveys. There was no strong evidence of bias.
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.
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 | 0.083 | 0.123 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".