Psychosocial predictors of adolescent girls' physical activity and dietary behaviours after completing the Go Girls! group-based mentoring program
Bibliographic record
Abstract
The current study applied the theory of planned behaviour to test social cognitions (i.e., affective and instrumental attitudes, subjective norms, self-regulatory efficacy, and intentions) targeted within a group-based mentoring program as predictors of both physical activity and dietary behaviour (separately) 7 weeks after participants completed the program. Data were collected from 237 participants at the end of, and 7 weeks after, completing the program. Multilevel structural equation modelling was used to assess both psychosocial (measured at the end of the program) and behavioural variables (i.e., diet and physical activity; measured 7 weeks after completing the program) among program participants. Analyses revealed that 36.5% and 31.2% of the variance in post-program physical activity and dietary behaviour was explained by affective and instrumental attitudes, self-regulatory efficacy, and intentions. Intentions mediated the effects of self-regulatory efficacy, affective and instrumental attitudes on physical activity behaviour. Similarly, in relation to dietary behaviour, intentions mediated the effects of self-regulatory efficacy, affective and instrumental attitudes. In conclusion, the results of this study provide insight into psychological factors that predict adolescent girls’ health-enhancing physical activity and dietary behaviours after they have left a group-based mentoring program.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".