Six ways to maximize survey response rates: lessons from a medical school accreditation survey in a Canadian setting
Bibliographic record
Abstract
Background: Surveys are being increasingly used to gather feedback and study data in healthcare professions. However, it may be challenging to achieve high response rates in surveys administered to healthcare professionals. The aim of this paper is to report six strategies that contributed to a high response rate on the Independent Student Analysis at the University of Toronto (U of T), which can be applied to other surveys to achieve strong response rates amongst healthcare professionals. Methods: In 2019, as part of accreditation for the U of T MD Program, we conducted the Independent Student Analysis, a student-led survey examining a medical student's experience. We review and critically evaluate the factors that contributed to a robust response rate amongst one of the largest cohorts of medical students in Canada. Results: Among 1080 students in the MD program, we achieved an unprecedented response rate of 87.2%. Six factors were identified that most contributed to our high response rate, including: faculty support, student representation, eliciting participant feedback, creating protected time for completion, offering incentives, and generating awareness. Conclusions: Eliciting high survey response rates from medical learners can be challenging. However, with careful consideration of learner feedback and effective employment of the strategies discussed in this paper, medical school faculty may better engage students in survey completion, achieving higher response rates and gathering richer insight, which can be used to more effectively enact meaningful change amongst healthcare professionals.
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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 | 0.654 | 0.681 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| 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; 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".