“Compulsive exercise is a socially acceptable prison cell”: Exploring experiences with compulsive exercise across social media
Bibliographic record
Abstract
OBJECTIVE: Investigations into online eating disorder (ED) communities have allowed for a rich exploration of lived experiences focused on a number of aspects, such as recovery and support groups. There is a lack of understanding around compulsive exercise (CE), which is often a characterizing condition of EDs. Exploring the lived experiences of CE as discussed online could provide helpful insight towards a better understanding of CE. Therefore, the purpose of this study was to explore experiences around CE and EDs shared on social media sites. METHOD: Social media posts related to CE and EDs from Reddit, Twitter, Instagram, and forums were collected for 12 months. A thematic analysis of 881 posts was used to identify common themes among individuals' lived experiences with CE. RESULTS: Five themes (and three subthemes) were identified across the social media posts: (1) seeking control, but ultimately CE takes hold, (2) burning off binges, but at what expense?, (3) recovery is a battle, but worth it, (4) is my exercise healthy?, and (5) frustration with comments about CE. DISCUSSION: The lived experiences of CE among individuals with EDs have provided support for current definitions of CE and shared novel insight into the recovery experience. Individuals online also highlighted the need for improvement in treatment around CE specifically, and greater awareness around CE for the general public and healthcare providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".