Evaluation of Fruit and Vegetable Consumption Habits Among Bankers in the Ho Municipality, Volta Region, Ghana
Bibliographic record
Abstract
Research findings show that insufficient fruit and vegetable intake increases the risk of cardiovascular diseases, stroke, diabetes, and obesity. However, many individuals across various age groups and professions fail to meet the recommended daily intake of 4–5 servings. One such underrepresented group is bankers. This study assessed fruit and vegetable consumption practices among 157 bankers from 12 banks in Ho Municipality. Findings revealed that 48.4% of participants were overweight, with males recording the highest frequency, while 40.8% were obese and only 10.8% had a normal BMI. Most respondents consumed 1-2 servings daily, falling short of the recommended intake. Pawpaw was the most consumed fruit, followed by bananas and oranges, while soursop and apples were least consumed. Many participants were unaware of the link between fruit and vegetable intake and health outcomes like diabetes, weight gain, and cardiovascular diseases. Financial constraints and high costs were notable barriers to consumption. Though no significant association was found between fruit intake and waist-to-hip ratio (p=0.212), the findings emphasise the increased cardiovascular risk, especially among male bankers. The study also acknowledges the need for targeted awareness campaigns to promote adequate fruit and vegetable intake among bankers to improve their health outcomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".