Psychosocial predictors of changes in adolescent girls’ physical activity and dietary behaviors over the course of the<i>Go Girls!</i>group-based mentoring program
Bibliographic record
Abstract
Changes in social cognitions targeted within a group-based mentoring program for adolescent girls were examined as predictors of changes in physical activity (PA) and dietary behavior (in two separate models) over the course of the 7-week program. Data were collected from 310 participants who participated in the program. Multilevel path models were used to assess changes in psychosocial variables predicting changes in behavioral outcomes from pre- to post-program. Analyses revealed that 24.4 and 12.3% of the variance in increases in PA and dietary behavior, respectively, was explained by increases in affective and instrumental attitudes, self-regulatory efficacy (SRE), and intentions. Increases in intentions partially mediated the effects of increases in SRE and affective attitudes on increases in PA behavior. In relation to improvements in dietary behavior, increases in intentions and SRE directly predicted improvements in dietary behavior. These findings suggest potential psychological mechanisms through which a group-based mentoring program may lead to changes in adolescent girls' health-enhancing PA and dietary behaviors.
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 | 0.001 | 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) | 0.001 | 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".