Psychological Treatment of Co‐Occurring Trauma History, Posttraumatic Stress Disorder, and Eating Disorders: A Systematic Review of Clinical Outcomes
Bibliographic record
Abstract
OBJECTIVE: The association between eating disorders (EDs) and both trauma exposure and posttraumatic stress disorder (PTSD) is well established. Existing evidence is inconclusive about the impact of trauma exposure and/or comorbid PTSD on ED treatment outcomes and if comorbid ED affects PTSD treatment outcomes. The objective of this systematic review is to consolidate the literature on treatment efficacy and effectiveness for individuals with EDs and trauma histories with and without PTSD, as well as to understand how ED and PTSD symptoms may impact improvement in each other, and how improvements in ED and PTSD symptoms might differ across treatment focus (ED-focused, PTSD-focused or both) and modality (e.g., cognitive behavioural, psychodynamic, transdiagnostic, integrative). METHODS: A systematic search of peer-reviewed publications was conducted across three databases (PsycINFO, PubMed, SCOPUS). RESULTS: Results indicate that comorbid PTSD symptoms, but not trauma history alone, may negatively affect ED treatment outcomes. Likewise comorbid ED symptoms may negatively affect PTSD treatment outcomes, but data is lacking. ED and/or PTSD symptom improvement was observed across treatment modalities. CONCLUSIONS: Results suggest that individuals may respond to a variety of modalities. Integrated and concurrent treatments show promise as an effective strategy to achieve long-term recovery from this debilitating comorbid condition.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".