Understanding the path to ketamine-assisted group therapy for firefighters diagnosed with post-traumatic stress disorder: a qualitative study
Notice bibliographique
Résumé
Introduction Firefighters face routine occupational risks and high-stress environments. Accordingly, 96.4% are exposed to a potentially traumatizing event throughout their career (Nazari et al. Work 2020; 67(1):215-222). This is accompanied by high rates of trauma-related mental illnesses (TRMI) such as PTSD. First-line treatments of TRMI remain limited. A novel treatment that has emerged, Ketamine-Assisted Therapy (KAT) has shown meaningful improvements in PTSD treatment (Ragnhildstveit et al.Ther Adv Psychopharmacol 2023; 6:13). Objectives The primary objective was to synthesize prevalent themes among participants, shedding light on their journeys to accessing KAT as well as highlighting internal motivations and societal barriers that exist. Methods In this study, there are six working firefighters, diagnosed with TRMI. They have enrolled in but not begun the 12-week Roots to Thrive (RTT) KAT program. Interviews were conducted individually and transcribed for further analysis. Data from the interview responses was analyzed by two independent reviewers and coded to create main themes of focus. The inconsistencies were managed by the third senior reviewer. Results The major themes prevalent among participants include social stigma, self-advocacy/education, logistical barriers and feeling stuck with previous treatments.Throughout the interviews, participants indicated that the use of ketamine and their mental illness diagnosis were stigmatized. Some kept their treatment a secret from family members while others reported telling very few colleagues about their PTSD. In response to traumatic events at work, one participant explained that the prevalent attitude was “Suck it up. Move on.” The lack of administrative support prompted several participants to teach themselves about psychedelics and mental health in order learn about treatment possibilities. Logistical barriers included barriers to physically commuting, paying for and having time away for the KAT sessions. They pointed out that aside from the price of the treatment itself, paying for cabs and ferries adds to the costs. Each of the participants detailed their journey with trauma and mental illness before KAT. A common sentiment expressed was that they couldn’t cope anymore with symptoms like night terrors and extreme anxiety. Participants discussed a gradual build-up and burnout, as one explained, “You continually are always going to other trauma calls, so it kind of builds up layer, layer, layer after layer.” A reoccuring theme in their experiences was trying different treatment modalities (EMT, EMDR and CBT) with little permanent success. Conclusions Greater funding, awareness and administrative support is needed for firefighters diagnosed with PTSD to successfully receive and undergo treatment. Disclosure of Interest None Declared
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,010 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».