Relationship-focused psychotherapies for eating disorders come of age.
Bibliographic record
Abstract
This is a commentary on 3 case studies of relationship-focused therapies for eating disorders. The 3 approaches vary along a number of dimensions, but nevertheless share important similarities especially related to the role played by variables such as interpersonal problems and affect dysregulation. I briefly review research on interpersonal- and attachment-based models of eating disorders that provide the evidence-base for theories of therapy that are relationship-focused. The Interpersonal Psychotherapy case presented by Tanofsky-Kraff, Shomaker, Young, and Wilfley (2016) illustrates how a group context can facilitate change in key role disputes and role transitions in an adolescent at risk of developing an eating disorder later in her life. The Integrative-Dynamic Therapy case presented by Richards, Shingleton, Goldman, Siegel, and Thompson-Brenner (2016) is a novel sequential combination of cognitive-behavioral therapy followed by dynamic psychotherapy for a young adult with bulimia nervosa that likely reflects what most clinicians do in everyday practice. The Psychoanalytic Psychotherapy case presented by Lunn, Poulsen, and Daniel (2016) of a patient with severe personality pathology demonstrates how treatments for eating disorders sometimes must address complex attachment dysfunction, self-organization, and therapist countertransference in order to provide a useful therapeutic experience. Relationship-focused theories and therapies for eating disorders have come a long way over the past decades, thus providing therapists with a wider range of approaches that can be truly personalized to their clients. (PsycINFO Database Record
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".