Are first responders interested in psychedelics? Assessing previous use, interest, and willingness to participate in psychedelic-assisted therapy
Bibliographic record
Abstract
Abstract Background and aims First responders such as firefighters and police officers often experience traumatic events as part of their work. As a result, they are more likely to have mental health issues such as post-traumatic stress disorder, depression, and anxiety compared to the general population. Psychedelic-assisted therapy has emerged as a promising avenue to alleviate these issues, but little is currently known about first responders' interest in, and barriers to, these treatments. Here, we aimed to document first responders' attitudes towards LSD-assisted therapy and previous use of psychoactive drugs. Methods We recruited 102 participants through mailing lists of first responders' unions. Respondents were typically male firefighters in western Canada; others were police officers, paramedics, and military personnel across Canada and the United States. They were asked about their attitudes towards LSD- and marijuana-assisted therapies, previous psychiatric diagnoses, psychosocial impairments, and substance use. Results Respondents showed higher rates of distress and illicit drug use compared to the general population. Of those who sought professional treatment, a minority reported that the treatment had helped them. The respondents were generally interested in taking part in therapy or research involving LSD or marijuana. The setting (e.g., at home vs. a clinic), therapist presence, and drug dose were commonly reported to influence this participation. Conclusions First responders may particularly benefit from psychedelic therapy given their high interest in psychedelic drugs and high rates of treatment-relevant disorders. Better understanding the needs of this population will help inform future clinical trials and psychedelic 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".