Psychosocial determinants of fruit and vegetable intake in adult population: a systematic review
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence suggests that fruit and vegetable intake (FVI) plays a protective role against major diseases. Despite this protective role and the obesity pandemic context, populations in Western countries usually eat far less than five servings of fruits and vegetables per day. In order to increase the efficiency of interventions, they should be tailored to the most important determinants or mediators of FVI. The objective was to systematically review social cognitive theory-based studies of FVI and to identify its main psychosocial determinants. METHODS: Published papers were systematically sought using Current Contents (2007-2009) and Medline, Embase, PsycINFO, Proquest and Thesis, as well as Cinhal (1980-2009). Additional studies were identified by a manual search in the bibliographies. Search terms included fruit, vegetable, behaviour, intention, as well as names of specific theories. Only studies predicting FVI or intention to eat fruits and vegetables in the general population and using a social cognitive theory were included. Independent extraction of information was carried out by two persons using predefined data fields, including study quality criteria. RESULTS: A total of 23 studies were identified and included, 15 studying only the determinants of FVI, seven studying the determinants of FVI and intention and one studying only the determinants of intention. All pooled analyses were based on random-effects models. The random-effect R2 observed for the prediction of FVI was 0.23 and it was 0.34 for the prediction of intention. Multicomponent theoretical frameworks and the theory of planned behaviour (TPB) were most often used. A number of methodological moderators influenced the efficacy of prediction of FVI. The most consistent variables predicting behaviour were habit, motivation and goals, beliefs about capabilities, knowledge and taste; those explaining intention were beliefs about capabilities, beliefs about consequences and perceived social influences. CONCLUSIONS: Our results suggest that the TPB and social cognitive theory (SCT) are the preferable social cognitive theories to predict behaviour and TPB to explain intention. Efficacy of prediction was nonetheless negatively affected by methodological factors such as the study design and the quality of psychosocial and behavioural measures.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".