Evidence-based assessment and treatment of military/Veteran PTSD: Reflections on 15 years of practice
Bibliographic record
Abstract
This reflection follows 15 years of military/Veteran clinical practice. It identifies special assessment and treatment considerations, with a focus on prolonged exposure therapy for posttraumatic stress disorder, and reviews common assumptions about evidence-based psychotherapy that can hinder practice and patient engagement. The heterogeneity of the Canadian military/Veteran population, and unique nature of military/Veteran service and its impact on mental health, is examined. Key recommendations include underscoring the importance of identifying the index trauma, using outcome monitoring, and understanding the theory of change for each of the gold-standard trauma-focused psychotherapies and the rationale for each intervention so that clinicians can individuate treatments for patients, as clinically required. Common misperceptions and controversies related to trauma-focused psychotherapies are discussed, including the goal of treatment and clinician hesitation to implement evidence-based psychotherapies. The intention of this reflection is to foster discussion, contribute to a greater understanding and deeper appreciation for the uniqueness of military/Veteran patients, and promote advancement and innovation in research and clinical practice.
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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 | 0.176 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".