Path to practising self‐compassion in a tertiary eating disorders treatment program: A qualitative analysis
Bibliographic record
Abstract
OBJECTIVE: Although self-compassion has been shown to facilitate eating disorder (ED) remission, significant barriers to acquiring this skill have been identified. This is particularly true for tertiary care populations, where ED behaviours provide a valued identity and readiness issues are highly salient. In this research, the voices and perspectives of patients who have recovered as well as those in later stages of tertiary care treatment were captured using qualitative methods. METHODS: Seventeen individuals with a lengthy ED history (seven fully recovered, 10 currently in recovery-focused residential treatment) participated in audio recorded interviews. Using a visual timeline, participants described the development of their understanding of self-compassion, barriers to self-compassion and how these barriers were overcome. RESULTS: Three processes were identified, reflecting different levels of readiness. Challenging my beliefs involved overcoming cognitive barriers to the concept of self-compassion (i.e. coming to see self-compassion as helpful), and set the stage for dealing with the world around me and rolling up my sleeves, which reflected preparatory (i.e. freeing oneself from difficult life circumstances) and active (i.e. having the courage to do the work) change efforts, respectively. CONCLUSIONS: These findings may help patients embarking on tertiary care treatment to envision a roadmap of supportive processes and help clinicians tailor interventions to patient level of readiness for self-compassion.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".