Obesity and Associated Factors Among Students of Different Medical Colleges in Cumilla During COVID-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has led to special situations and changes to daily life due to the worldwide measures that were brought into effect such as lockdowns. Obesity is a major public health concern among medical students which are undesirable health condition and its frequency is high in this Covid -19 pandemic situation. The aim of the study was to assess the state of obesity and associated factors among students of three medical colleges located in Cumilla district during COVID-19 pandemic situation. Methods: This study was a cross-sectional study; Purposive sampling technique was used to select 325 students from three different medical colleges of Cumilla. Data were collected from participants through face-to-face interview using a semi-structured questionnaire after taking informed written consent. Data were analyzed by SPSS software. Results: Among the respondents 52.3% were low, 24.3% were moderate, 24.3%, 12% were high and 11.4% were no physical activity. About 1.8% took one time 12.9% took two times 55.7% took three times 29.5% took their meal more than three times per day. Majority of respondents 54.2% drunk 4 to 6 glasses water daily. Among the 325 participants 12.6% were obese, 21.2% were pre- obesity. Normal BMI was 44.9% and 21.2% was underweight. Obesity was associated with sex; as female medical students were observed to have significantly higher BMI compare to those with male respondents (P<0.000). Family type of the students from joint family were observed more obese than nuclear family, (P<0.05), Dietary pattern. as the BMI of the respondents increased with the increase of frequency of monthly fast food consumption of the respondents (P<0.04). Female medical students were significantly higher biscuits consumption (P<0.03) but male respondent inversely significantly higher in cold drink consumption (P<0.00). Conclusion: The study findings may contribute to developing awareness about weight gain and its long term health consequence and devising interventions to prevent COVID-19 related weight gain among medical students. JOPSOM 2024; 43(2): 53-60
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 | 0.001 |
| 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".