Overweight, Obesity and its Associated Factors among Nurses at Tertiary Care Hospitals Karachi
Bibliographic record
Abstract
Overweight and obesity have been identified as considerable health risks worldwide. Objective: To identify the prevalence of overweight, and obesity and its association with demographic variables among nurses. Methods: A cross-sectional analytical study was conducted at Dr. Ruth KM Pfau Civil Hospital and Dow University Hospital Karachi over a period of six months of periods from March to August 2019. A total of 299 subjects of both genders were approached by the non-probability convenient sampling method. Chi-square test was applied to identify the associated factors. P-value ≤ 0.05 counted as significant. Results: Out of 299, half of the study nurses 149 (49.8%) were male. Among 299 participants, 75 (25.1%) of them were overweight or obese. While 13 (4.3%) were underweight and 211 (70.6%) were normal weight. Mean age, working experience, and BMI were found 29.52 ± 8.568, 7.35 ± 6.177, and 23.30 ± 3.148 respectively of the study nurses. Gender (p-value=0.003), educational status (p-value=0.002), and nature of the job (p-value=0.003) of the participants were found statistically significant with BMI. Conclusions: Present study concluded that the majority of study participants had normal BMI and a small number of study subjects were found obese. However, a quarter of nurses are recognized as overweight. Moreover, a significant association was established between BMI with gender, the nature of the job, and the education of nurses.
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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 | 0.000 | 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".