Physical Activity and Social Cognitive Theory: A Test in a Population Sample of Adults with Type 1 or Type 2 Diabetes
Bibliographic record
Abstract
The purpose of the study was to test the Social Cognitive Theory (SCT; ) for explaining physical activity (PA) in a large population sample of adults with type 1 or type 2 diabetes. Study objectives: (1) test the fit of the SCT structure in the total sample, and the diabetes sub‐types; (2) determine the SCT structural invariance between the type 1 and type 2 groups; and (3) report explained variance and compare strength of association for the SCT constructs in predicting PA for both type 1 and type 2 groups. In all, 2,311 individuals with type 1 or type 2 diabetes were assessed on their self‐efficacy, outcome expectancies, impediments, social support, goals, and physical activity at baseline and 1,717 (74.5%) completed these assessments again at 6 months. Multi‐group Structural Equation Modeling was conducted. The findings provide evidence for the utility of the SCT in the diabetes samples. The SCT fits individuals with type 1 and type 2 diabetes except for SCT impediments, which appear to be obstructing goal‐setting in individuals with type 2 diabetes only. Promotion of health behavior should target self‐efficacy to set goals and change behavior. Outcome expectancies and social support are also important factors for setting goals and behavior performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".