Predictors of physical activity, healthy eating and being smoke-free in teens: A theory of planned behaviour approach
Bibliographic record
Abstract
This paper elicited context specific underlying beliefs for physical activity, fruit and vegetable consumption and smoke-free behaviour from the Theory of Planned Behaviour (TPB), and then determined whether the TPB explained significant variation in intentions and behaviour over a 1 month period in a sample of grade 7-9 (age 12-16 years) adolescents. Eighteen individual interviews and one focus group were used to elicit student beliefs. Analyses of this data produced behavioural, normative and control beliefs which were put into a TPB questionnaire completed by 183 students at time 1 and time 2. The Path analyses from the main study showed that the attitude/intention relationship was moderately large for fruit and vegetable consumption and small to moderate for being smoke free. Perceived behavioural control had a large effect on being smoke free and a moderately large effect for fruit and vegetable consumption and physical activity. Intention had a large direct effect on all three behaviours. Common (e.g. feel better, more energy) and behaviour-specific (e.g., prevent yellow fingers, control my weight) beliefs emerged across the three health behaviours. These novel findings, to the adolescent population, support the importance of specific attention being given to each of the behaviours in future multi-behavioural interventions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".