Weight bias reduction in health professionals: a systematic review
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.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".