Multiple group identifications and identity compatibility in eating disorder recovery: A mixed methods study
Bibliographic record
Abstract
Abstract Eating disorder recovery is an identity transition characterised by ambivalence, in which group memberships play an important part. However, our understanding of how memberships of groups with different recovery norms (i.e., supportive vs. unsupportive of recovery) can facilitate or inhibit recovery is limited. To address this gap, this study adopted the Social Identity Model of Recovery to examine how recovery is manifest through the changing composition of an individual's group memberships. We employed a convergent mixed methods design to quantitatively determine whether specific groups (i.e., family, friends, and online groups) are more helpful to eating disorder recovery than others, and to qualitatively explore how group (in)compatibility shapes recovery efforts. There was a high level of convergence across survey ( N = 112) and interview ( N = 12) data: groups could have a positive or negative impact according to their recovery norms; different groups provided different forms of support and identity‐expression; incompatibility was not always experienced as a problem and could afford strategic benefits. Our findings are amongst the first to attest to the importance of considering identity networks (and their normative content) during eating disorder 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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".