Workforce Diversity in Eating Disorders: A Multi-Methods Study
Bibliographic record
Abstract
Despite growing recognition of the importance of workforce diversity in health care, limited research has explored diversity among eating disorder (ED) professionals globally. This multi-methods study examined diversity across demographic and professional variables. Participants were recruited from ED and discipline-specific professional organizations. Participants' (n = 512) mean age was 41.1 years (SD = 12.5); 89.6% (n=459) of participants identified as women, 84.1% (n = 419) as heterosexual/straight, and 73.0% (n = 365) as White. Mean years working in EDs was 10.7 years (SD = 9.2). Qualitative analysis revealed three themes resulting in a theoretical framework to address barriers to increasing diversity. Perceived barriers were the following: "stigma, bias, stereotypes, myths"; "field of eating disorders pipeline"; and "homogeneity of the existing field." Findings suggest limited workforce diversity within and across nations. The theoretical model suggests a need for focused attention to the educational pipeline, workforce homogeneity, and false assumptions about EDs, and it should be tested to evaluate its utility within the EDs field.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".