Understanding physical activity intentions among French Canadians with type 2 diabetes: an extension of Ajzen's theory of planned behaviour
Bibliographic record
Abstract
BACKGROUND: Regular physical activity is considered a cornerstone for managing type 2 diabetes. However, in Canada, most individuals with type 2 diabetes do not meet national physical activity recommendations. When designing a theory-based intervention, one should first determine the key determinants of physical activity for this population. Unfortunately, there is a lack of information on this aspect among adults with type 2 diabetes. The purpose of this cross-sectional study is to fill this gap using an extended version of Ajzen's Theory of Planned Behavior (TPB) as reference. METHODS: A total of 501 individuals with type 2 diabetes residing in the Province of Quebec (Canada) completed the study. Questionnaires were sent and returned by mail. RESULTS: Multiple hierarchical regression analyses indicated that TPB variables explained 60% of the variance in intention. The addition of other psychosocial variables in the model added 7% of the explained variance. The final model included perceived behavioral control (beta = .38, p < .0001), moral norm (beta = .29, p < .0001), and attitude (beta = .14, p < .01). CONCLUSION: The findings suggest that interventions aimed at individuals with type 2 diabetes should ensure that people have the necessary resources to overcome potential obstacles to behavioral performance. Interventions should also favor the development of feelings of personal responsibility to exercise and promote the advantages of exercising for individuals with type 2 diabetes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".