Prospective evaluation of direct approach with a tablet device as a strategy to enhance survey study participant response rate
Bibliographic record
Abstract
BACKGROUND: Investigators conduct survey studies for a variety of reasons. Poor participant response rates are common, however, and may limit the generalizability and utility of results. The objective of this study was to determine whether direct approach with a tablet device enhances survey study participant response rate and to assess participants' experiences with this mode of survey administration. FINDINGS: An interventional study nested within a single center survey study was conducted at McMaster Children's Hospital. The primary outcome was the ability to achieve of a survey study response rate of 70% or greater. Eligible participants received 3 email invitations (Week 0, 2, 4) to complete a web-based (Survey Monkey) survey. The study protocol included plans for a two-week follow-up phase (Phase 2) where non-responders were approached by a research assistant and invited to complete an iPad-based version of the survey. The Phase 1 response rate was 48.7% (56/115). Phase 2 effectively recruited reluctant responders, increasing the overall response rate to 72.2% (83/115). On a 7-point Likert scale, reluctant responders highly rated their enjoyment (mean 6.0, sd 0.83 [95% CI: 5.7-6.3]) and ease of use (mean 6.7, sd 0.47 [95% CI: 6.5-6.9]) completing the survey using the iPad. Reasons endorsed for Phase 2 participation included: direct approach (81%), immediate survey access (62%), and the novelty of completing a tablet-based survey (54%). Most reluctant responders (89%) indicated that a tablet-based survey is their preferred method of survey completion. CONCLUSIONS: Use of a tablet-based version of the survey was effective in recruiting reluctant responders and this group reported positive experiences with this mode of survey administration.
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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 | 0.210 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".