“<i>When Are We Going to Hold Orthorexia to the Same Standard as Anorexia and Bulimia?</i>” Exploring the Medicalization Process of Orthorexia Nervosa on Twitter
Bibliographic record
Abstract
This study contributes to understanding medicalization on social media, by using Conrad’s concept of medicalization as a theoretical framework to explore the conversation about Orthorexia Nervosa (ON) on Twitter. The aim of this mixed-methods study was twofold: the quantitative component aimed to provide descriptive information on the type of tweets and users, as well as on the network structure of the ON-related conversation on Twitter, while the qualitative component aimed to explore how the medicalization of ON unfolds on Twitter by performing a thematic analysis of original tweets about ON. Quantitative descriptive findings show that the most popular hashtags associated with orthorexia include #rdchat, #psychology and #doctors, which hints to a link between discourses around ON and the medical profession. Among the most active, prominent and visible users are news accounts, a registered dietitian, a researcher, a professor and an editor. Qualitative thematic analysis shed light on the discursive process of medicalization. Some users bring about medicalization by approaching ON as a medical entity; in contrast, other users resist medicalization by describing ON as a social phenomenon. A discursive struggle emerges, where certain individuals feel confused around what constitutes ON. This leads to stigmatization of non-traditional diets like veganism, which in turn triggers complaints regarding over-medicalization. As the first Twitter investigation on ON, this study serves the purpose of providing insights into how an emerging disorder develops in society in a time of social media.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".