A Cohort-Based Case Report: The Impact of Ketamine-Assisted Therapy Embedded in a Community of Practice Framework for Healthcare Providers With PTSD and Depression
Bibliographic record
Abstract
Amid an international pandemic and a worsening mental health crisis, ketamine-assisted therapy is emerging as a promising solution for those deemed "treatment resistant." Post-traumatic stress disorder (PTSD) and depression are on the rise, with accelerating direct (e.g., burden of suffering) and indirect (e.g., disability/role impairment and impact on family) costs. Psychedelic-assisted therapies show significant promise in the treatment of a number of clinically challenging conditions, including depression, anxiety, PTSD, addiction, and end-of-life distress. Ketamine is currently the only safe, effective and legal widely available psychedelic-like medicine. To address the echo pandemic of health care provider distress, a multi-disciplinary team was charged with developing a ketamine-assisted psychotherapy program, delivered in a community of practice (CoP) group model and evaluated in a quality improvement framework. Program evaluation occurred through mixed methods. Quantitative mental health assessments included the PHQ-9 for depression, the PCL-5 for PTSD, GAD-7 for generalized anxiety disorder (GAD), and B-IPF for work/life functionality. Participant narrative feedback was collected to evaluate outcomes and for quality improvement purposes. Mean mental health scores were collected across three cohorts, totaling 94 patients. The mean aggregate scores of participants meeting the mental health assessment cut-off criteria (screening positive) were analyzed to assess clinical significance. Mean aggregate results comparing baseline vs. outcome measures (measured within 1-2 weeks after completion of the 12-week program) were clinically significant, demonstrating significant improvements in depression, post-traumatic stress disorder, generalized anxiety disorder and work/life functionality. In summary, 91% saw improvements in generalized anxiety, 79% saw improvements in depression, 86% of those who screened positive for PTSD now screen negative, and 92% had significant life/work functionality improvements. Qualitative feedback was overwhelmingly positive, with several unsolicited self-reports of transformation. Participant and team feedback enables the program to continue improving with each iteration. Results speak to the effectiveness of ketamine for psychedelic-assisted therapy, supported by a CoP framework. Outcomes are relevant for mental health programming, education and healthcare policy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".