The Effect of Ketamine-Assisted Group Therapy on Treatment-Resistant Mental Health Conditions in Firefighters
Bibliographic record
Abstract
OBJECTIVE: Firefighters display elevated risk for diagnoses of mental health illnesses. Psychedelic-assisted therapies show promise in the treatment of clinically challenging conditions. This observational case series analyzed data from firefighters with mental health diagnoses who participated in a 12-week ketamine-assisted group therapy treatment plan. METHODS: Questionnaire scores (Generalized Anxiety Disorder Assessment-7, Patient Health Questionnaire-9, The Posttraumatic Stress Disorder Checklist -5, Brief Inventory of Psychosocial Functioning ) collected throughout the program were scored and statistically analyzed for changes. Qualitative data were analyzed through thematic analysis. RESULTS: Significant decreases with large effect sizes were detected in Generalized Anxiety Disorder Assessment-7 and The Posttraumatic Stress Disorder Checklist-5 scores after completion in the 12-week treatment plan, which persisted 6 months later. Participants noted it was beneficial being in a cohort with fellow firefighters. CONCLUSIONS: Statistically and clinically significant improvements to posttraumatic stress disorder and anxiety diagnoses were detected in the cohort of firefighters after the Roots to Thrive Ketamine-Assisted Group Therapy program, with results retained six months post treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".