Using delphi consensus methods to understand which physical activity behaviour change theories should be taught to Canadian undergraduate students
Bibliographic record
Abstract
While theories of physical activity (PA) behaviour change are taught to undergraduate students in a range of programs, there is little guidance on which theories should be taught prior to graduation. Aim: To determine which PA behaviour change theories are recommended to be taught to undergraduate students in PA and/or exercise science by the time they graduate. Methods: A Delphi consensus exercise was completed by instructors from across Canada who taught PA behaviour change to undergraduate students. In Round 1, 18 professors completed an online questionnaire to generate a list of PA theories taught. In Round 2, 15 instructors indicated their level of agreement using an 11-point Likert scale as to whether the theories from Round 1 should be taught. In Round 3, 12 instructors were presented a refined list of theories that received high consensus in Round 2 (Mean Score: >7 and rated >7.0 by >66% of participants) and indicated their level of agreement. Results: Round 1 identified 39 theories. After Round 2, 9 theories met consensus guidelines which was further refined to 5 theories in Round 3. However, interclass correlations statistics revealed little consistency in theory ratings. Conclusions: A wide range of theories are taught to undergraduate students and there is a lack of consensus among experts as to which theories should be taught to students. Findings will inform the instruction of behavior change theories in PA and/or exercise science at an undergraduate level.
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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 | 0.133 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".