Expanding a single-institution survey to multiple institutions: Lessons learned in research design and deployment
Bibliographic record
Abstract
Objective: Creating generalizable knowledge across institutions is a step beyond a successful local research project. The purpose of this article is to share the process and lessons learned from expanding a survey tool developed and piloted at a single veterinary college to its deployment at multiple veterinary colleges in the United States and Canada. Population or problem: Little guidance exists on expanding a survey developed for a single institution to distribution to health professions students across multiple institutions. Methods: In June 2016, the first author of the survey contacted librarians from veterinary colleges to explore a possible multi-institution study to investigate student behaviors and perceptions around scientific information. Librarians from twenty-nine institutions initially expressed interest. Those at fifteen institutions participated in initial planning, and eight elected to distribute the survey. Of these, seven submitted for IRB review at their own institution and one institution facilitated the distribution of the survey under the original institution’s IRB exemption. Findings: The IRB submission process and requirements varied by participating institution. Mean time from submission to approval was 10 days (range: 2-31 days). Several changes were made to the survey based on the recommendations of participating librarians, ranging from simplifying the method of survey distribution to modifying specific questions to make them meaningful across institutions. As participating institutions did not have synchronized academic calendars, the survey distribution took a staggered approach between institutions based on IRB review and varying institutional processes. Conclusions: Expanding even a simple IRB-exempt survey from one institution to others requires careful consideration of local practices, attention to differences in the IRB process, and ethical considerations for recruiting students where librarians serve as instructors or hold other positions of influence. Attempts to standardize recruitment messaging and survey questions for generalizable results required compromise by the librarian researchers at participating institutions.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".