A Pilot Study Comparing a Community of Practice Program with and without Concurrent Ketamine-Assisted Therapy
Bibliographic record
Abstract
The prevalence of depression, anxiety, and post-traumatic stress disorder (PTSD) has increased among healthcare providers, while the effectiveness of conventional treatments remains limited. Ketamine-assisted therapy offers a promising alternative; however, few have integrated ketamine with a group-based therapeutic modality. We report a retrospective, secondary analysis of a 12-week pilot of a Community of Practice (CoP) oriented group therapy program with optional, adjunct ketamine for depression, anxiety, and PTSD in a sample of 57 healthcare providers. All participants moved through the treatment as one group, with 38 electing to also receive three adjunct ketamine sessions in addition to the weekly CoP. Symptoms were assessed at baseline and pilot completion with the PHQ-9 for depression, GAD-7 for anxiety, and PCL-5 for PTSD. We observed significant reductions in the mean change among all participants, suggesting that benefit was derived from the CoP component, with or without ketamine as an adjunct. PHQ-9 scores decreased by 6.79 (95% CI: 5.09–8.49, p < .001), GAD-7 scores decreased by 5.57 (CI: 4.12–7.00, p < .001), and PCL-5 scores decreased by 14.83 (CI: 10.27–19.38, p < .001). Reductions were larger, but statistically nonsignificant, among those receiving ketamine. Further research is required to assess the impact of ketamine as an adjunct in group-based therapies.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".