Exploring Attachment, Trauma, and Cannabis Use in Psychotic Disorders: A Qualitative Study of Patient and Family Perspectives
Notice bibliographique
Résumé
Background: Psychotic disorders are debilitating mental illnesses that affect individuals in physical, emotional, psychological, and social ways. Both biological and environmental factors are thought to play a role in illness occurrence and severity. Previous studies suggest that insecure attachment, trauma, and cannabis use are major environmental factors contributing to the severity of psychotic illness. Despite the known vulnerabilities created by these risks, little is known about the understanding that patients and families have with respect to these risks. It remains unclear how the interplay of these risks unfolds, creates pathways of vulnerability, and whether these pathways are recognized and addressed by patients and their families. While many researchers and clinicians are aware of these problems, it seems that not all patients are, indicating a disconnect in knowledge translation between patients and healthcare providers. Research also highlights the critical role that family members play in recovery for those with psychotic disorders, making their perspectives an important tool to consider in clinical treatment. Although resources for patients and family members currently exist, recovery remains challenging, prompting the emergence of specialized clinics focused solely on psychotic disorders, such as the Early Psychosis Intervention Program (EPIP) in Saskatoon, SK. Therefore, the primary aim of this study is to qualitatively examine the understanding that patients and family members have regarding these risks in relation to their illness, and to explore the role of the EPIP clinic in their recovery. Method: Patients and family members were recruited from the EPIP clinic at Royal University Hospital or from the Schizophrenia Society of Saskatchewan. Semi-structured qualitative interviews were conducted with patients experiencing first-episode psychosis (17) and their family members (9). Interviews were recorded, transcribed, coded, and analyzed using thematic analysis based on Braun and Clarke’s six-phase framework. An inductive, reflexive, constructivist approach was utilized in the analysis. Results: Five major themes were generated 1) Cannabis use: From early appeal to lasting harm; 2) Shifts in relationships mirror shifts in recovery; 3) When it comes to risk factors for psychosis, more is always more; 4) Clear as mud: Patients’ and families’ understanding of things that matter; and 5) The rocky road to recovery: From initial confusion to final healing. The final themes reflect mixed levels of understanding regarding the risks from both patients and family members. The study suggests that those who are more aware of the risks and implement changes to address them— such as quitting cannabis, developing stable and trustworthy relationships, and adopting a trauma-informed approach — seem to recover better than those who do not. Overall, the current study reflects that the literature might not always accurately translate into the lives of those affected, highlighting a need for clinical steps to address this gap in knowledge translation. Furthermore, the unanimous success of the EPIP clinic is clearly evident in all the patients and family members interviewed.
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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 | 0,011 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,012 | 0,009 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».