Self-compassion and its barriers: predicting outcomes from inpatient and residential eating disorders treatment
Bibliographic record
Abstract
BACKGROUND: Individuals with eating disorders (EDs) experience barriers to self-compassion, with two recently identified in this population: Meeting Standards, or concerns that self-compassion would result in showing flaws or lead to loss of achievements or relationships, and Emotional Vulnerability, or concerns that self-compassion would elicit difficult emotions such as grief or anger. This exploratory study examined the utility of self-compassion and two barriers to self-compassion in predicting clinical outcomes in intensive ED treatments. METHOD: Individuals in inpatient (n = 87) and residential (n = 68) treatment completed measures of self-compassion and fears of self-compassion, and ten clinical outcome variables at pre- and post-treatment. RESULTS: Pre-treatment self-compassion was generally not associated with outcomes, whereas pre-treatment self-compassion barriers generally were. In both treatment settings, fewer Emotional Vulnerability barriers were associated with improved interpersonal/affective functioning and quality of life, and fewer Meeting Standards barriers were associated with improved readiness and motivation. Interestingly, whereas Meeting Standards barriers were associated with less ED symptom improvement in inpatient treatment, Emotional Vulnerability barriers were associated with less ED symptom improvement in residential treatment. CONCLUSIONS: Given that few longitudinal predictors of outcome have been established, the finding that pre-treatment barriers to self-compassion predict outcomes in both inpatient and residential settings is noteworthy. Targeting self-compassion barriers early in treatment may be helpful in facilitating ED 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".