Alcohol Intake by Workers in a Health Care Institution in Bucaramanga, Colombia
Bibliographic record
Abstract
The harmful alcohol intake represents a global problem. Its high consumption has been associated with cardio metabolic risk factors. Evaluating their consumption in health workers is important for the formulation of strategies to promote healthy lifestyles. The objective of this study was to determine the consumption of alcohol and establish the differences of this intake in terms of socio-demographic and cardiovascular characteristics of interest in hospital workers of Bucaramanga, Colombia. An analytical cross sectional was made (baseline of an intervention study to reduce cardiovascular risk factors). Sociodemographic, anthropometric, biochemical, physical activity, and lifestyles characteristics, as well as alcohol consumption (g / week) were evaluated using a previously validated Frequency Alcohol Questionnaire. Multiple linear regression models were used, adjusting for sex, age, socio-economic level, schooling and marital status. 77.4% (95% CI: 71.2% to 82.8%) of the study participants consumed some type of alcoholic beverage during the month prior to the survey, with an average of 70.0 grams of alcohol per week of 70.0 g. We found a statistically significant difference (p = 0.012) of 40.4 grams of alcohol per week (95% CI: 8.9 to 71.8 g / week) consumed among those who have hypertriglyceridemia and those who do not. In conclusion, the high consumption of grams of alcohol per week is related to a triglyceride level above the normal ranges.
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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 | 0.001 | 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".