Results of the 2024 International Weight Bias Summit: Establishing future research directions in the field
Bibliographic record
Abstract
BACKGROUND: Weight bias is a social justice issue that manifests in social and health inequities, affecting the lives of millions of individuals globally. Although weight bias research has increased over the last two decades, it remains pervasive, and more work is needed to establish effective strategies to reduce it. The 2024 International Weight Bias Summit aimed to collaboratively identify future research directions for prioritization as well as perceived barriers in the global field of weight bias and stigma. This paper presents the primary findings from the Summit. METHOD: Experts in weight bias (N = 33 researchers, clinicians, representatives of professional/national organizations with interests in weight bias and stigma, and individuals with lived experiences) from across North and Latin America, Europe, and Australia attended the two-day Summit. Attendees participated in semi-structured small group discussions using the Nominal Group Technique (NGT). Notes were collected from all discussions and thematically analyzed to identify the most prominent research directions and barriers that emerged from the Summit. RESULTS: Experts identified six key research directions (presented without hierarchical ranking): (1) consequences of weight bias, (2) conceptual and methodological clarity, (3) diversity in sampling, cultures, and settings, (4) interventions, (5) policy, and (6) implementation science. Three key barriers were also identified in weight bias and stigma research: (1) widespread misconceptions and lack of recognition of weight bias as a legitimate issue, (2) funding challenges, and (3) lack of collaborations and working in silos. CONCLUSION: Experts identified six critical research directions that should be prioritized to advance weight bias and stigma research and drive meaningful progress. Continued international collaboration was recognized as essential to driving this work forward.
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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 | 0.245 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".