Weight bias reduction in health professionals: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Innovative and coordinated strategies to address weight bias among health professionals are urgently needed. We conducted a systematic literature review of empirical peer-reviewed published studies to assess the impact of interventions designed to reduce weight bias in students or professionals in a health-related field. Combination sets of keywords based on three themes (1: weight bias/stigma; 2: obesity/overweight; 3: health professional) were searched within nine databases. Our search yielded 1447 individual records, of which 17 intervention studies satisfied the inclusion criteria. Most studies (n = 15) included medical, dietetic, health promotion, psychology and kinesiology students, while the minority included practicing health professionals (n = 2). Studies utilized various bias-reduction strategies. Many studies had methodological weaknesses, including short assessment periods, lack of randomization, lack of control group and small sample sizes. Although many studies reported changes in health professionals' beliefs and knowledge about obesity aetiology, evidence of effectiveness is poor, and long-term effects of intervention strategies on weight bias reduction remain unknown. The findings highlight the lack of experimental research to reduce weight bias among health professionals. Although changes in practice will likely require multiple strategies in various sectors, well-designed trials are needed to test the impact of interventions to decrease weight bias in healthcare settings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.023 |
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 it