Examining the Interplay between Body Mass Index (BMI) Categories and Self-Esteem in the Workplace: Implications for Psychological Safety and Employee Wellbeing
Bibliographic record
Abstract
This study delves into the intricate relationship between Body Mass Index (BMI) categories and self-esteem within workplace dynamics. In contemporary society, mental attributes are increasingly valued alongside personality traits. Self-esteem, a fundamental psychological construct, significantly influences individuals' actions, thoughts, and behaviors. However, societal norms often perpetuate stigmas surrounding body types, leading to varying degrees of acceptance. Criticism based on body weight can profoundly impact both mental and physical well-being. Employing the Self-esteem Scale by Dr. Santosh Dhar and Dr. Upinder Dhar, alongside BMI calculations derived from recorded weight and height measurements, this study analyzed a sample of 100 male and 100 female subjects. Results indicated that male subjects exhibited higher self-esteem levels than their female counterparts. Furthermore, individuals classified as Obese displayed the highest self-esteem, followed by Overweight, Normal, and Underweight subjects, respectively. Additionally, married individuals demonstrated higher self-esteem levels compared to unmarried individuals. This study underscores the significance of understanding the interplay between BMI categories and self-esteem within the workplace environment. The findings highlights the requirement for workplace initiatives that foster for psychological safety and body inclusivity such that it reduces weight-related stigma and promoting supportive organizational cultures may improve employee mental health wellbeing.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".