“<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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it