An Illustration of a Methodology to Maximize Mail Survey Response Rates in a Provincial School-Based Physical Activity Needs Assessment
Bibliographic record
Abstract
Abstract: Two mail surveys were conducted as a province-wide needs assessment to examine the opportunities for, barriers to, and participation in physical activity in Ontario elementary and secondary schools. Dillman’s Tailored Design Method (TDM) was used to maximize the quality of responses and the response rate. Both surveys entailed five mailings to key informants from randomly selected schools. The response rate among the 599 elementary and 600 secondary schools was 85% and 79%, respectively. This article discusses how the TDM strategies (i.e., strategies to establish trust, increase perceived rewards, and decrease perceived costs among key informants) were used to yield high response rates, examines the response rates after each mailing, and shares some lessons learned, which will be useful to researchers who are considering using the TDM in surveys designed to develop and evaluate programs.
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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 | 0.420 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".