Eating Disorders in Males: How Primary Care Providers Can Improve Recognition, Diagnosis, and Treatment
Bibliographic record
Abstract
Eating disorders are complex and multifactorial illnesses that affect a broad spectrum of individuals across the life span. Contrary to historic societal beliefs, this disorder is not gender-specific. Lifetime prevalence of eating disorders in males is on the rise and demanding the attention of primary care providers, as well as the general population, in order to negate the potentially life-threatening complications. Current literature has continued to reinforce the notion that eating disorders predominately affect females by excluding males from research, thereby adding to the void in men-centered knowledge and targeted clinical care. To determine what is currently known about eating disorders among males, a scoping review was undertaken, which identified 15 empirical studies that focused on this topic. Using the Garrard matrix to extract and synthesize the findings across these studies, this scoping review provides an overview of the contributing and constituting factors of eating disorders in males by exploring the associated stigmas, risk factors, experiences of men diagnosed with an eating disorder, and differing clinical presentations. The synthesized evidence is utilized to discuss clinical recommendations for primary care providers, inclusive of male-specific treatment plans, as a means to improving care for this poorly understood and emerging men's health issue.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".