A Qualitative Study of the Lived Treatment Experiences of Women With an Eating Disorder and Comorbid Borderline Personality Disorder
Bibliographic record
Abstract
OBJECTIVE: Having both an eating disorder (ED) and borderline personality disorder (BPD) is associated with heightened clinical complexity, high levels of distress, and challenges in treatment. This study sought to qualitatively investigate the experiences of women with an ED and comorbid BPD as they undergo ED treatment, aiming to better understand factors that shape their perceptions of care. METHODS: Fourteen women with both an ED and BPD in treatment at a public-sector ED clinic were recruited to participate in an open-ended qualitative interview about their lived treatment experiences. RESULTS: Five overarching themes (with seven subthemes) emerged from the qualitative analysis: (1) Difficulties with emotions as a key factor underlying both ED and BPD; (2) Perceptions of BPD in ED maintenance and treatment; (3) Relational dynamics in treatment; (4) Treatment is "never enough"; and (5) The importance of treating the ED and BPD together. DISCUSSION: This study highlights the lived experiences of women with both an ED and BPD. Patients expressed the need for ED treatment to target emotion dysregulation, interpersonal difficulties, and their attachment to their therapists and to treatment. This study provides insights into the experiences of patients with BPD of ED treatment that may help guide the approach to care in such individuals.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".