The Effective Factors for Fruit and Vegetable Consumption among Adults: A Need Assessment Study Based on Trans-Theoretical Model
Bibliographic record
Abstract
INTRODUCTION: The World Health Organization recommended consuming at least 5 servings of fruits and vegetables (FV) per day in order to reduce the risk of non-communicable diseases (NCDs). The purpose of this study is to determine the influential factors related to intake of FV among adults in Kermanshah city based on Transtheoritical Model. MATERIAL & METHODS: This is a cross-sectional study which is conducted in Kermanshah city. Participants (n=1230) are selected by multi stage sampling; 30-50 year olds people covered by health centers. In order to collect data, we used a TTM-based questionnaire. The results are analyzed using SPSS-16 and Lisrel 8, with P< 0.05 as statistically significant level. RESULTS: The mean age of the participants is 37.75 and 65% of them are women .The mean score of knowledge is 2.4; that is, 80% of men and 78% of women in this study are in poor knowledge about FV consumption. In case of fruit and vegetable consumption behavior, 50% and 61% of participants are in pre-contemplation/contemplation stage, respectively. The average number of fruit servings is 1.42 and the average number of vegetable servings is 0.99 per day. Also, ANOVA test results showed a significant correlation between constructs of TTM and stages of change so that individuals' progress through stages of change from pre-contemplation to maintenance added on the scores of self-efficiency, processes of change, and decisional balance. CONCLUSION: This study indicated that, TTM constructs such as self-efficacy, processes of change, and decisional balance are good predictors for FV consumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".