Reimagining Public Engagement in Eating Disorders Research
Bibliographic record
Abstract
A review and bibliometric analysis on the last five decades of eating disorders (EDs) research by Lee and Chi (2025) reports a generally weak connection between public attention and academic citations. The authors suggest a few potential reasons for this phenomenon, including that public interest may not reflect long-term scientific value. We use a public health policy perspective to offer an alternate argument: that public attention and engagement, alongside scientific rigor, are necessary to move the ED field forward and generate substantive policy change. We discuss how research topics that resonate with the public, such as less commonly represented EDs or EDs among under-represented populations, can lead to decreases in stigma and support the early identification of symptoms among the population. Drawing on the ideas of strategic science, we stress the importance of intentionally linking academic work to policy by conducting research that is relevant to decision-makers as well as the broader public, working within multi- and trans-disciplinary teams, and training researchers, especially those in early career positions, on how to conduct policy-relevant research. The synergism of public engagement with research and scientific impact can be a powerful force for moving the ED field 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 | 0.418 | 0.644 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.054 | 0.064 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.054 | 0.057 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".