Increasing Participation and Completion Rates in Questionnaire Surveys of Primary Care Patients: Cluster-Randomized Study
Bibliographic record
Abstract
BACKGROUND: Participation and completion rates in questionnaire-based surveys are often low. OBJECTIVE: This study aims to assess participation and completion rates for a survey using paper and mixed mode questionnaires with patients recruited by research assistants in primary care waiting rooms. METHODS: This cluster-randomized study, conducted in 2023 in France, involved 974 patients from 39 practices randomized into 4 groups: "paper with incentive" (n=251), "paper without incentive" (n=368), "mixed mode with tablet" (n=187), and "mixed mode with QR code" (n=168). Analyses compared the combined paper group with the 2 mixed mode groups and the "paper with incentive" and "paper without incentive" groups. Logistic regressions were used to analyze participation and completion rates. RESULTS: Of the 974 patients recruited, 822 (women: 536/821, 65.3%; median age 52, IQR 37-68 years) agreed to participate (participation rate=84.4%), with no significant differences between groups. Overall, 806 patients (98.1%) answered all 48 questions. Completion rates were highest in the combined paper group (99.8%) compared to mixed mode groups (96.8% for paper or tablet, 93.3% for paper or QR code; P<.001). There was no significant difference in completion rates between the "paper with incentive" and "paper without incentive" groups (100% vs 99.7%). CONCLUSIONS: Recruiting patients in waiting rooms with research assistants resulted in high participation and completion rates across all groups. Mixed mode options did not enhance participation or completion rates but may offer logistical advantages. Future research should explore incentives and mixed-mode strategies in diverse settings.
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How this classification was reachedexpand
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 | 0.240 | 0.337 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".