Collaborative care in eating disorders treatment: exploring the role of clinician distress, self-compassion, and compassion for others
Bibliographic record
Abstract
BACKGROUND: Collaborative care is described as showing curiosity and concern for patient experiences, providing choices, and supporting patient autonomy. In contrast, in directive care, the clinician has authority and the patient is expected to adhere to a treatment plan over which they have limited influence. In the treatment of eating disorders, collaborative care has been shown to be more acceptable and produce better outcomes than directive care. Despite widespread patient and clinician preference for collaborative care, it is common for clinicians to be directive in practice, resulting in negative patient attitudes toward treatment and poor adherence. There is a need to understand factors which contribute to its use. PURPOSE: This study examined the contribution of clinicians' experience of distress and how they relate to themselves and others in times of difficulty (self-compassion and compassion for others), to their use of collaborative support. METHOD: Clinicians working with individuals with eating disorders from diverse professional backgrounds (N = 123) completed an online survey. RESULTS: Whereas clinician distress was not associated with use of collaborative or directive support behaviours, self-compassion and compassion for others were. Regression analyses indicated that compassion for others was the most important determinant of collaborative care. DISCUSSION: Relating to their own and others' distress with compassion was most important in determining clinicians' use of collaborative support. Understanding how to cultivate conditions that foster compassion in clinical environments could promote the delivery of collaborative care.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".