Social structure, social cognition, and physical activity: A test of four models
Bibliographic record
Abstract
OBJECTIVE: This study investigated the combined influence of social structural factors (e.g. income) and cognitions in predicting changes in physical activity. Four models were tested: (a) direct effects (social structural factors influence behaviour controlling for cognitions), (b) mediation (cognitions mediate social structural influence), (c) moderation (social structural factors moderate cognition-behaviour relations), and (d) mediated moderation (cognitions mediate the moderating effects of social structural position). DESIGN: Baseline and 3-month follow-up surveys. METHODS: A random sample of 1,483 adults completed self-report measures of physical activity at baseline and 3-month follow-up. Measures of age, gender, education, income, material and social deprivation, intention, perceived behavioural control (PBC), and intention stability also were taken. RESULTS: Apart from age, social structural factors exhibited very small or marginal effects on behaviour change, and only education moderated the intention-behaviour relation. In contrast, the magnitude of direct effects of the social cognition variables was comparatively large and intention stability mediated the moderating effect of education. CONCLUSIONS: Stable intentions and PBC are the key predictors of changes in physical activity. Consequently, our findings would suggest the value of focusing on cognitions rather than social structural variables when modelling the determinants of physical activity.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".