An investigation into the relevance of action planning, theory of planned behaviour concepts, and automaticity for fruit intake action control
Bibliographic record
Abstract
OBJECTIVES: In the action control framework, intention-behaviour discordance is studied around public health guidelines. Although this framework has been applied to physical activity behaviours, it has only seen very limited attention regarding fruit intake. The purpose of this study was therefore to investigate distributions and predictors of fruit intake intention-behaviour discordance. DESIGN: Prospective correlational design. METHODS: Data were obtained from undergraduate students (n = 413) using validated questionnaires. Variables from the theory of planned behaviour, automaticity, and action planning were assessed at baseline, and fruit intake was assessed 2 weeks later. Data were analysed using discriminant function analyses and analyses of variance. RESULTS: The proportion of unsuccessful intenders ranged from 39.2% to 80.8%. There was a larger proportion of fruit intake intenders amongst those who reported strong automatic fruit intake. Action control was predicted by fruit intake automaticity and affective attitudes, but the strongest predictor was perceived behavioural control. No action planning items were related to fruit intake action control. CONCLUSIONS: There is considerable asymmetry in the intention-fruit intake relationship. An application of the action control framework may stimulate debate on the applicability of intention-based models at the public health level. STATEMENT OF CONTRIBUTION: What is already known on this subject? Intention is theorized to be a key construct in fruit intake. Studies in the physical activity domain indicate that nearly half of the people with positive intentions fail to subsequently act. What does this study add? The proportion of unsuccessful intenders ranged from 39.2% to 80.8%. Holding positive intentions is not sufficient to consume fruit at suggested public health guidelines. Perceived behavioural control is the most important predictor of fruit intake action control.
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".