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
Abstract Background Annually, there are least 235,000 individuals experiencing homelessness in Canada. These individuals are more likely to have complex health issues, including mental health issues such as post-traumatic stress disorder (PTSD). Diagnosed PTSD rates in the homeless are more than double that of the general population, ranging between 21% and 53%. In the homeless population, complex PTSD (cPTSD) appears to be more common than PTSD. One treatment option for cPTSD is Narrative Exposure Therapy (NET), a brief trauma focused psychotherapy which attempts to place the trauma within a narrative of the person’s life. Previous studies suggest NET may be an effective option for those who are homeless. In this study, our primary aim was to assess the feasibility and acceptability of delivering community-based NET to individuals with PTSD who were homeless or vulnerably housed. Methods This pilot randomized controlled trial (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 either NET alone or NET plus the addition of a genealogical assessment. Demographic and clinical data were collected at the baseline visit. Symptoms of PTSD, drug use and housing status were re-assessed at follow-up visits. Rates of referral, consent and retention were also examined as part of feasibility. Results Twenty-two potential participants were referred to the study. Six were not able to be contacted, one was excluded prior to contact, and the remaining 15 consented 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 One randomized participant was withdrawn for safety. The main point of attrition was prior to starting therapy (3/13). Once therapy was initiated, retention was high with 80% of participants completing all six sessions of therapy. Seven participants completed all follow-up sessions. Conclusion Delivering NET in a community-based setting and completing genealogical assessments was both feasible and acceptable to those who are homeless or vulnerably housed. Once therapy had been initiated, participants were likely to stay engaged. A large RCT should be conducted to evaluate effectiveness and feasibility on an increased scale. Trial Registration clinicaltrials.gov , identifier NCT03781297 . Registered: December 19, 2018, https://clinicaltrials.gov/ct2/show/NCT03781297 . Key messages regarding feasibility What uncertainties existed regarding the feasibility? Narrative Exposure Therapy (NET) is an individual trauma focused psychotherapy recommended in guidelines for the treatment of post-traumatic stress disorder (PTSD). However, in people who are homeless or vulnerably housed, there have been no randomised controlled trials of trauma focused therapies for PTSD. We wanted to find out if it was feasible to recruit and retain people who were homeless with PTSD in a randomized controlled trial of a trauma informed therapy. We wanted to test the acceptability and feasibility of offering NET alone compared to NET plus a genealogical assessment. We also wanted to see if it was acceptable and practical to incorporate a genealogical assessment as part of NET. What are the key feasibility findings? The key feasibility finding is that it is feasible to recruit and retain people who are homeless into a randomized controlled trial of a trauma informed therapy. However, recruitment could be improved by a better process for engaging potential participants between referral and enrollment as about a third of the referred population were lost at this stage. Not having trained therapists available also delayed recruitment. What are the implications of the feasibility findings for the design of the main study? We will explicitly develop materials for use at the referral step by potential referrers. This will include an online training video which will address issues of trust and how to address them when discussing the potential study. We will also develop a process for training and recruiting therapists. Since this study was completed, we have done two further training workshops to create a pool of potential therapists in Ottawa. We will also engage with another site in Ontario to widen the population base of potential participants.
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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 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".