Ketamine-Assisted Therapy Outcomes for First Responders With Comorbid Mental Health Diagnoses
Bibliographic record
Abstract
OBJECTIVE: Many first responders deal with mental health challenges due to significant work-related stress. Ketamine-assisted therapy (KAT) has been shown to alleviate the symptoms and improve the lives of those living with PTSD. This qualitative case study explores the impacts of KAT on the firefighters beyond the initial alleviation of their PTSD symptoms. METHODS: Six firefighters who were diagnosed with PTSD participated in a 12-week KAT program, which combined several virtual Community of Practice meetings as well as in-person small group ketamine sessions. Participants were interviewed about their journey to receiving treatment, and a follow-up survey was also sent. RESULTS: The novel themes that emerged from this study include improvement in sleep problems, relationship to music, improved tolerance for sensory stimuli, and changes in time perception. CONCLUSIONS: KAT may provide meaningful benefits beyond symptom reduction.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".