Interpersonal psychotherapy versus treatment as usual for PTSD and depression among Sichuan earthquake survivors: a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Without effective treatment, PTSD and depression can cause persistent disability in disaster-affected populations. METHODS: Our objective was to test the efficacy of Interpersonal Psychotherapy (IPT) delivered by trained local personnel compared with treatment as usual (TAU) for Posttraumatic Stress Disorder (PTSD) and Major Depressive Disorder (MDD) among adults affected by the Sichuan 2008 earthquake. A small randomized controlled trial of IPT + TAU versus TAU alone was delivered by local mental health personnel in Shifang, China. Between July 2011 and January 2012, 49 adults ≥ 18 years with PTSD, MDD or both were enrolled and randomized to 12 weekly sessions of IPT + TAU (27) or TAU (22) alone x 12 weeks. IPT was then offered to the TAU group. Unblinded follow up assessments were conducted at three and six months. IPT was a 12 session, weekly one hour treatment delivered by local personnel who were trained and supervised in IPT. TAU was continuation of prescribed psychotropic medication (if applicable) and crisis counseling, as needed. MAIN OUTCOME(S) AND MEASURES (S): Clinician Administered PTSD Scale (CAPS) PTSD diagnosis; Structured Clinical Interview for DSM-IV (SCID) for MDD diagnosis. Secondary measures included PTSD/depression symptoms, interpersonal conflict/anger, social support, self-efficacy and functioning. RESULTS: Using an intent-to-treat analysis, 22 IPT + TAU and 19 TAU participants were compared at three months post-baseline. A significantly greater reduction of PTSD and MDD diagnoses was found in the IPT group (51.9%, 30.1%, respectively) versus the TAU group (3.4%, 3.4%, respectively). Despite the small sample, the estimates for time-by-condition analyses of target outcomes (2.37 for PTSD (p = .018) and 1.91 for MDD (p = .056)) indicate the improvement was better in the IPT + TAU condition versus the TAU group. Treatment gains were maintained at 6 months for the IPT group. A similar treatment response was observed in the TAU group upon receipt of IPT. CONCLUSIONS: This initial study shows that IPT is a promising treatment for reducing PTSD and depression, the two major mental health disorders affecting populations surviving natural disaster, using a design that builds local mental health care capacity. TRIAL REGISTRATION: ClinicalTrials.Gov number, NCT01624935.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".