“This is just how I cope”: An inductive thematic analysis of eating disorder recovery content created and shared on <scp>TikTok</scp> using #<scp>EDrecovery</scp>
Bibliographic record
Abstract
OBJECTIVE: To explore eating disorder (ED) recovery-related content created and shared on the social media platform TikTok. METHOD: A systematic review and inductive thematic analysis of 150 TikTok posts catalogued under hashtag (#) EDrecovery. Two coders independently analyzed the posts and a critical peer facilitated discussions about the resulting codes and themes. RESULTS: Creators on TikTok used #EDrecovery to share their personal experiences with recovery through the use and cooption of popular (or viral) video formats, succinct storytelling, and the production of educational content. Five themes were interpreted across the data: (a) ED awareness, (b) inpatient story time: "ED unit tings", (c) eating in recovery, (d) transformations: "how about a weight gain glow up?", and (e) trendy gallows humor: "let's confuse people who have a good relationship with food". DISCUSSION: TikTok as a user-friendly, creative media may provide the artistic and social tools for some creators to add their distinct voice to the ED recovery narrative and foster some semblance of community. Although all of the analyzed content was catalogued under #EDrecovery, some of the posts reified the increasingly blurred boundary that exists between ED recovery and pro-ED content on TikTok.
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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 | 0.047 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".