A Weight‐Inclusive Approach to Restrictive Eating Disorders: De‐Centering Weight
Bibliographic record
Abstract
OBJECTIVE: To examine the case for de-centering weight in the diagnosis of both anorexia nervosa (AN) and atypical anorexia nervosa (AAN). METHOD: We summarized research examining the weight-based similarities and differences between AN and AAN as well as how weight is measured and discussed in research on AAN. RESULTS: We suggest that weight-based differences in the diagnosis of AN and AAN may unintentionally perpetuate weight stigma experienced by individuals with AAN throughout the diagnostic and treatment processes. We extend the work of previous researchers by considering how AAN and AN might be considered as one disorder that occurs across the weight spectrum, with presentation and severity specifiers that remove a focus on BMI and enable more equitable access to care for those with AAN. DISCUSSION: We propose further examination and scholarly discussion for the conceptualization of AN and AAN as one disorder that occurs across the weight spectrum.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".