Disclosing an Eating Disorder: A Situational Analysis of Online Accounts
Bibliographic record
Abstract
Disclosing a mental illness can be difficult, especially for those affected by eating disorders. Individuals impacted by eating disorders often worry that disclosing their situation may lead to fear, judgment, and stigmatization. Online eating disorder communities have become increasingly popular, hosting thousands of users worldwide, and may be safe places for individuals with eating disorders to communicate and connect. In this postmodern study, we utilized situational analysis to examine online accounts on publically accessible websites where individuals discussed disclosing eating disorders. Situational Analysis utilizes illustrative mapping techniques to demonstrate the complexity of the situation of inquiry, allowing researchers to highlight heterogeneities. Our findings demonstrated (a) the fight that frequently occurs after an eating disorder disclosure, (b) the notion that eating disorders are a monstrous issue, and (c) stigmatization one experiences after disclosing and when considering to disclose. This study has potential to inform educational recommendations given to the public about disclosures and stigma in regard to eating disorders, as well as earlier identification and treatment outcomes for individuals with eating disorders.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".