Teaching Behavior Change Theory in Canada: Establishing Consensus on Behavior Change Theories That Are Recommended to Be Taught to Undergraduate Students in Courses Addressing Health Behavior Change
Bibliographic record
Abstract
There is little guidance on which behavior change theories should be taught in undergraduate courses addressing health behavior change. Delphi consensus methods provide a formal, systematic, and reproducible method for establishing consensus among experts. Objective. Use a Delphi methodology to establish consensus regarding behavior change theories that should be taught to undergraduate students enrolled in health behavior change courses. Method. An online Delphi consensus exercise was completed by instructors who were identified through a systematic search of 94 University course calendars to be teaching health behavior change content to undergraduate students in Canada. In Round 1, 22 participants generated a list of theories taught in undergraduate courses. In Rounds 2 and 3, participants indicated their level of agreement using an 11-point Likert-type scale as to which theories should be taught. Theories that reached predetermined consensus criteria were retained in each round. Results. In Round 1, participants listed over 50 different theories being taught in undergraduate courses. After Round 2, nine theories met consensus criteria which were refined to only six theories in Round 3 (i.e., behavior change wheel, self-determination theory, self-efficacy theory, social ecological model, social cognitive theory, theory of planned behavior). Conclusions. A wide range of theories are taught in undergraduate courses. However, only a minority of these theories reached consensus criteria as being theories that should be taught to undergraduate students enrolled in courses addressing health behavior change. Findings can be used to improve the consistency and quality of instruction of behavior change theories at the 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 | 0.185 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".