Physical activity and dietary patterns: a health risk behavior cluster pattern analysis of students in a Caribbean medical school
Bibliographic record
Abstract
Background: Collegial effects of variable lifestyle risk behaviors on the high incidence of chronic conditions are pivotal issues in defining overall health and public wellness. Medical students are expected to have a superior understanding of health issues but the majority of them lead an unhealthy lifestyle. Aims and Objectives: This study examined the prevalence and clustering patterns of multiple health risk behaviors among students of a Caribbean medical school. Materials and Methods: A cross-sectional study was conducted among the first and second-year medical students by using questionnaire which assessed multiple health behaviors including physical activity patterns, fruits, vegetables and breakfast consumption. Age and gender specific clustering patterns of various risk behaviors were identified. Results: Study suggested that male students were more active than female students (p<0.01). Gender non-specific younger age group was more active than older age group (p<0.001). The frequency of breakfast consumption was higher in males (p<0.01). Fruit & vegetable consumption was higher in older students (p<0.01) irrespective of gender. A cluster of three health risk behavior was found in 10% of the total students while only 7% met the recommendations for all three health risk behaviors. BMI of the majority of students (72%) was found to be within normal range. The primary motivation for performing physical activity in 35% students was to lose weight or maintain appearance and in 31% to eliminate stress. Conclusion: Many medical students still fail to meet the physical activity and dietary recommendations which may be attributed to their busy schedules and academic overload. Asian Journal of Medical Sciences Vol.8(4) 2017 50-56
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".