Knowledge translation in biostatistics: a survey of current practices, preferences, and barriers to the dissemination and uptake of new statistical methods
Bibliographic record
Abstract
BACKGROUND: The use of standard statistical methods in the medical literature has been studied extensively; however, the adoption of new methods has received less attention. We sought to understand (i) whether there is a perception that new methods are underused, (ii) what the barriers to use of new methods are, (iii) what dissemination activities are used, and (iv) user preferences for learning about new methods. METHODS: We conducted a cross-sectional survey of members of the Statistical Society of Canada (SSC) and of principal investigators (knowledge-users) funded by the Canadian Institutes of Health Research (CIHR). RESULTS: There were 157 CIHR respondents (14% response rate), and 39 respondents were statisticians from the Statistical Society of Canada. Seventy percent of CIHR respondents and 82% of statisticians felt that new developments were under-used. Barriers to use of new methods included lack of access to the necessary expertise (selected by over 90% of respondents), lack of suitable software (selected by 81% of statisticians), and lack of time to implement new methods (selected by 78% of statisticians). Greater access to statistical colleagues with an interest in collaboration and availability of software to implement new methods were the top-rated preferences among knowledge-users. CONCLUSIONS: There was a clear perception among all respondents that new statistical methods are underused. Encouraging statistical methodologists to develop a knowledge translation plan for improved dissemination and uptake, placing greater value on the role of the statistical collaborator in research, and providing software alongside new methods may improve the use of newly developed statistical methods.
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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 | 0.119 | 0.479 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".