Trauma focused psychotherapy in patients with suicidal ideation: A scoping review
Bibliographic record
Abstract
This scoping review focused on the use of trauma focused therapy (TFT) with participants who have suicidal ideation. Methods: Following the PRISMA extension for Scoping Reviews guidelines, MEDLINE, EMBASE, APA PsycINFO, and CINAHL databases were searched on March 18, 2021. Peer-reviewed studies in English reporting on the use of TFT with patients with suicidal ideation or Borderline Personality Disorder (BPD) were included. Results: From 3,272 publications, 43 studies were included. Most studies utilized Prolonged Exposure, Eye Movement Desensitization and Reprocessing, Cognitive Processing Therapy, and other exposure-based interventions, alone or in combination with another intervention. Approximately 50% of studies used intensive (two sessions or more per week) delivery of the intervention. Studies mainly focused on clinical improvement of symptoms, rather than suicidality. Overall, studies reported symptom improvements in Posttraumatic Stress Disorder, depression, and BPD symptoms, suicidal ideation and non-suicidal self-injury, with few critical adverse events on record. Conclusion: Despite increased research interest in this area, knowledge gaps remain. Greater attention to mixed methods studies may increase our understanding of the lived experience of those with suicidal ideation undergoing TFT. There is a need for studies to explore the effect of TFT on symptoms associated with psychiatric diagnoses other than PTSD, and for studies reporting on the significance of both sex and gender of patients. Prospective interventional studies that focus on participants with suicidal ideation, and consensus on standardized suicidal ideation outcome measures, are also needed. There is a need to compare intensive vs. non-intensive TFT, and to examine whether inclusion of emotion regulation skills is a significant determinant of suicidal risk outcomes in this context. In contrast to common apparent clinical practice decisions restricting TFT to patients without suicidal symptoms, limited evidence indicates that TFT, including intensive delivery, may not increase suicide risk. Due to methodological issues, further studies are needed to confirm this observation and to determine any increased risk for specific patient subgroups and TFT interventions.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".