#recovery: Understanding recovery from the lens of recovery‐focused blogs posted by individuals with lived experience
Bibliographic record
Abstract
OBJECTIVE: Definitions of eating disorder (ED) recovery have primarily focused on symptom management (i.e., weight regain, reduced/absent ED behaviors, and normalized ED thoughts). Notwithstanding the importance of these approaches, there are arguably additional considerations in ED recovery. In order to get a more comprehensive understanding of recovery, it is necessary to turn to individuals with lived experience. Here, we examine how individuals with lived experience of an ED conceptualize and define recovery in narrative, recovery-focused blogs and consider how this understanding may contribute to definitions of recovery in the field. METHOD: Inductive thematic analysis was used to examine 168 blogs posted by at least 120 unique authors (95% women; 36% reporting anorexia nervosa diagnosis) to 10 moderated, ED websites. RESULTS: Results from the thematic analysis yielded seven themes: recovery as (1) existing in contrast to the ED, (2) existing in a broader context, (3) subjective, (4) a choice, (5) a complex, nonlinear process, (6) transformative, and (7) overcoming. DISCUSSION: The present findings are consistent with previous qualitative research, suggesting that recovery is multifaceted and encompasses more than just symptom management. Notably, bloggers highlighted that recovery may not be equally attainable for all individuals, citing numerous social justice issues in the conceptualization of recovery. This multifaceted and intersectional view of recovery is consistent with consumer models of recovery. We argue that a dimensional model of recovery may be a good starting framework for researchers and clinicians to develop a more comprehensive definition of recovery.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".