Gender and sex in eating disorders: A narrative review of the current state of knowledge, research gaps, and recommendations
Bibliographic record
Abstract
INTRODUCTION: Eating disorders (EDs) have long been considered conditions exclusively affecting women, and studies in the ED field regularly exclude men. Research efforts are needed to better understand the role of gender and sex in EDs. This review describes the role of gender and sex in the development of EDs from a biopsychosocial perspective. METHODS: The primary hypothesis of this narrative review is that gender and sex interact to influence ED risk. The literature review was conducted using the PubMed database. RESULTS: This review first presents the general characteristics and prevalence of EDs according to gender and sex. Next, neurodevelopmental processes, neurobiology, gender roles, body image, and the minority stress model are addressed. Lastly, research perspectives to better include gender and sex in the field of EDs are discussed (e.g., representation of gender and sex diversities, development of appropriate assessment tools, and increasing awareness). CONCLUSION: Although substantial knowledge gaps remain, there is a growing recognition of the importance of integrating gender and sex in ED research that holds promise for further development in the field.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".