The Awareness of Abdominal Obesity and Metabolic Syndrome in Healthcare Workers
Bibliographic record
Abstract
Background: In a study that has been done five years ago, it was reported that too few of healthcare workers were aware of abdominal obesity and metabolic syndrome (MetS) as a clinical entity. The aim of this study was to evaluate if any difference in abdominal obesity and MetS awareness in healthcare staff working in the same hospitals was occurred in the past 5 years. Methods : A total of 731 healthcare workers (physicians: 262, nurses: 199, other healthcare staff: 270, mean age: 32.17 ± 8.0) were enrolled. Demographic, anthropometric, and biochemical data were recorded. International Diabetes Federation (IDF) criteria were used for abdominal obesity and MetS assessment. Results : The frequency of abdominal obesity and awareness of abdominal obesity was 32.5% (36.6% in women, 29.7% in men, P = 0.050) and 16.7% (18.7% in physicians, 9.6% in nurses, 3.8% in other healthcare staff, P = 0.001) respectively. The awareness of MetS as a clinical entity was 31.3% (78.6% in physicians, 11.1% in nurses, 0.4% in other healthcare staff, P = 0.001). The frequency of MetS was 6.1% (3.7% in women, 10% in men, P = 0.015). Conclusions: In this research, it has been found out that for the past five years, still very few of the healthcare workers are aware of the MetS and abdominal obesity as a clinical entity. J Endocrinol Metab. 2013;3(3):57-61 doi: https://doi.org/10.4021/jem176w
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".