Relationship between Health Workers' Body Mass Index, Emotional Eating, And Uncontrolled Eating
Bibliographic record
Abstract
Background: Obesity and overweight are significant public health concerns globally, with healthcare professionals being particularly vulnerable due to high job demands and stress. The relationship between body mass index (BMI), job stress, and eating behaviors remains underexplored in this population, despite evidence linking workplace stress to unhealthy eating habits and weight gain. Methods: This study, conducted over three months , assessed the relationship between BMI, job stress, and eating behaviors among 400 male and female healthcare professionals. Participants were classified into normal, overweight, and obese BMI categories based on measured weight and height. Data were collected using the Brief Job Stress Questionnaire and the Adult Eating Behaviour Questionnaire (AEBQ). Statistical analysis, including descriptive statistics and Spearman’s rank correlation, was performed using SPSS version 28, with a significance threshold of p ≤ 0.05. Results: Among the participants, 230 had normal BMI, 110 were overweight, and 60 were classified as obese (class 1 and class 2). Physiotherapists reported the highest levels of job stress, particularly in the normal BMI category, while dental professionals experienced the least. Eating behaviors were elevated in 260 participants, with mean AEBQ scores varying across BMI categories. Despite the high prevalence of job stress and altered eating behaviors, the correlation between BMI, job stress, and eating behavior was weak and not statistically significant. Conclusion: The study highlights that while job stress and unhealthy eating behaviors are prevalent among healthcare professionals, their direct correlation with BMI remains inconclusive. Lifestyle factors such as physical activity and dietary habits may play a more critical role in influencing BMI, underscoring the need for targeted interventions to promote healthier lifestyles in this population.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".