STIGMATIZATION AND DISCRIMINATION OF OBESE PATIENTS BY HEALTHCAREWORKERS: A GLOBAL HEALTHCARE ISSUE
Bibliographic record
Abstract
Obesity is a chronic, metabolic disease that stems from an imbalanced calorie intake and can be influenced by genetic, environmental and individual factors. Obesity and its complications are among the major global health issues of the 21st century. Many studies have confirmed that obese individuals evoke negative emotions in others, such as disgust, repulsion or even anger. Search was performed on two databases: PubMed and Google Scholar. The following keywords were used: “obesity”, “obese”, “patient”, “stigma”, “weight bias”, “healthcare”, “healthcare professionals”, “medical professionals”, “discrimination”, “fatphobia”. The majority of the articles come from 2013-2023, and no language restriction was applied. Medical personnel often display negative attitudes toward obese patients, which negatively affects the patient’s health and the quality of received care. Currently available literature suggests the occurrence of obesity and weight stigma in many countries around the globe, such as: Poland, Germany, Brazil, USA, Canada, Mexico, Singapore, Israel, and Australia. Both medical personnel and medical students display examples of stigma behaviors. Despite the prevalence of obesity, people with excessive body weight often face social disapproval and discrimination. This stigmatizing behavior can also occur among medical personnel. There is a need to eliminate these negative attitudes and beliefs within the medical community.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: yes | Systematic review | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".