Exploring the self-perceived causes of eating disorders among Chinese social media users with self-reported eating disorders
Bibliographic record
Abstract
Abstract Background Even though robust evidence suggests the high prevalence of Eating disorders (EDs) in China, EDs in China are characterized by low diagnosis rates, delayed treatment-seeking, and ineffective treatments. Given that listening to patients’ perspectives and lived experiences is crucial to improving our understanding of EDs in the Chinese context, an investigation of the perceived causes of EDs in Chinese individuals with EDs represents a key step in improving the prevention and treatment of EDs in China. Aims To explore the perceived causes of eating disorders (EDs) based on the data from the users of a popular Chinese social media platform, Zhihu. Methods We extracted and analyzed the data using a content analysis. Eight specific causes that could be classified into two groups were coded, including individual factors (e.g., “body image and eating”) and sociocultural factors (e.g., “media and cultural ideals”). Results A total of 2079 entries regarding self-reported EDs were retained for content analysis (14.7% were anorexia nervosa, 37.6% were bulimia nervosa, and 47.7% were binge-eating disorder). More than 90% of users with self-reported EDs claimed causes belonging to individual factors, while 35–51% of users claimed sociocultural factors. “Body image and eating” (68–87%) and “psychological and emotional problems” (65–67%) were the most commonly claimed specific causes, while “traumatic life events” (13–14%), “genetics and biology” (7–13%), and “sports and health” (9–12%) were the least claimed. Chi-square independent tests showed that users with different self-reported EDs disproportionately claimed certain causes. Conclusions Using large-scale social media data, findings enhanced our understanding of the perceived etiology of EDs in the Chinese context from individuals with lived experiences of EDs and highlighted the variations in perceived causes across different self-reported ED types.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".