Fruit and Vegetable Consumption among Special School Students with Mild Intellectual Disability in Hong Kong
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to predict the fruit and vegetable consumption intention of students with mild intellectual disability in Hong Kong by the application of Ajzen's Theory of Planned Behaviour. METHODS: 50 students with mild intellectual disability (30 male and 20 female), ranging in age from 15 to 38 years, were participated in this study. By means of face-to-face interviews, demographic data, Food Preference and variables of Theory of Planned Behaviour, such as Attitude, Subjective Norm and Perceived Behavioural Control were measured. RESULTS: 20%, 28% and 10% students with mild intellectual disability were rated to be overweight, obese and severely obese respectively. The rest of 10% were classified to be underweight. Regarding the daily intake of fruit and vegetable, 96% students with mild intellectual disability failed to consume sufficient amount. The variables of Theory of Planned Behaviour explained 47.7% of fruit and vegetable consumption intention with significant factors of Attitude, Subjective Norm and Perceived Behavioural Control. Food Preference was found to be a useful construct and further improve the prediction by about 7% after incorporating into the model. CONCLUSIONS: Results of this study indicated that Theory of Planned Behaviour is a useful model to predict dietary intention of students with mild intellectual disability in Hong Kong. Food Preference was a significant predictor to model the intention of fruit and vegetable consumption among students other than Attitude, Subjective Norm and Perceived Behavioural 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".