The road to behaviour is paved with good intention...and willpower! Intentions and trait self-control predict fruit and vegetable consumption during the transition to first-year university
Bibliographic record
Abstract
Health behaviours such as physical activity and fruit and vegetable consumption (FVC) decline in early adulthood (Baranowski et al.,1999). While the Theory of Planned Behavior (TPB) can be used to predict such behaviours, moderators have been suggested (Conner & Armitage,1998). As FVC may require self-monitoring, trait self-control (TSC) may moderate the intention-behavior relationship (Crescioni et al.,2011). The purpose of this study was to examine the independent and combined effects of intention and TSC on FVC among first-year students. We hypothesized that the TPB would predict FVC intentions and behaviour, and TSC would be associated with higher FVC. In their first week at university, students (N=76, Mage=18.00±0.49) completed the 13-item Brief TSC (Tangney et al.,2004) and a TPB questionnaire (Ajzen,1991) about their FVC. One week later, they completed a 7-day food diary, from which daily FVC was calculated. Attitude (ß=.20) and PBC (ß=.64, ps=.02) were significant predictors of intentions (adjR2=.34). Intentions (ß=.45) and TSC (ß=.21, ps=.05) predicted FVC (adjR2=.24), but PBC and interactions were not significant. Findings support the TPB and further our understanding of factors associated with health behaviours during young adulthood. Although intentions and TSC were linked to FVC, most students ate
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".