Cognitive Behavioral Therapy for Trauma and Self-Care to Treat Posttraumatic Stress Symptoms and Support HIV Care Engagement Among Men With HIV Who Have Sex With Men: A Case Series
Bibliographic record
Abstract
There is a strong need for clinical interventions that improve engagement in HIV care among men who have sex with men (MSM) with HIV who have histories of trauma. Cognitive behavioral therapy (CBT) has substantial support for treating posttraumatic stress among people with HIV and among HIV-negative MSM. In this population, posttraumatic responses can include both general avoidance of distress related to the trauma but also specific avoidance of HIV-related stimuli that can reduce engagement in HIV care. The present paper introduces an application of Cognitive Behavioral Therapy for Trauma and Self-Care (CBT-TSC), which integrates CBT strategies to address posttraumatic stress symptoms with the Life-Steps framework, an evidence-based, single-session problem-solving intervention that increases adherence to antiretroviral therapies. Other CBT components include psychoeducation and the reduction of specific trauma symptoms, including avoidance and negative self-beliefs that can act as barriers to care engagement. The intervention is presented via four individual case studies. These cases demonstrate the ways in which CBT-TSC can be used to support mental health and self-care among MSM with HIV who have histories of trauma and are facing barriers to full participation in HIV care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".