Feasibility and acceptability of Narrative Exposure Therapy to treat individuals with PTSD who are homeless or vulnerably housed: a pilot randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Diagnosed PTSD rates in people who are homeless are more than double that of the general population, ranging between 21 and 53%. Complex PTSD (cPTSD) also appears to be more common than PTSD. One treatment option is Narrative Exposure Therapy (NET), a brief trauma-focused psychotherapy which attempts to place trauma within a narrative of the person's life. Our primary aim was to assess the feasibility and acceptability of recruiting people to a randomized controlled trial (RCT) of NET alone compared to NET augmented by a genealogical assessment. We hypothesized that incorporating a genealogical assessment may be more effective than NET alone in a population with predominately complex PTSD. METHODS: This pilot RCT enrolled participants who were 18 years of age or older, currently homeless or vulnerably housed, and with active symptoms of PTSD. Participants were randomized to NET alone or NET plus a genealogical assessment. Rates of referral, consent, and retention were examined as part of feasibility. Demographic and clinical data were collected at baseline. Symptoms of PTSD, drug use, and housing status were re-assessed at follow-up visits. We conducted a thematic analysis of qualitative interviews of service providers involved in the study which explored barriers and facilitators of study participation. RESULTS: Twenty-two potential participants were referred to the study, with 15 consenting to participate. Of these, one was a screen failure and 14 were randomized equally to the treatment arms. One randomized participant was withdrawn for safety. Attrition occurred primarily prior to starting therapy. Once therapy began, retention was high with 80% of participants completing all six sessions. Seven participants completed all follow-up sessions. Service providers identified a clear need for the treatment and emphasized the importance of trauma-informed care, a desire to know more about NET, and more communication about the process of referral. CONCLUSION: Recruiting participants who were vulnerably housed to an RCT of a trauma-based therapy was possible. Once therapy had started, participants were likely to stay engaged. We will incorporate the results of this trial into a conceptual model which we will test in a factorial study as part of the optimization phase of MOST. TRIAL REGISTRATION: ClinicalTrials.gov NCT03781297 . Registered: December 19, 2018.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".