Influence of military component and deployment-related experiences on mental disorders among Canadian military personnel who deployed to Afghanistan: a cross-sectional survey
Notice bibliographique
Résumé
OBJECTIVE: The primary objective was to explore differences in mental health problems (MHP) between serving Canadian Armed Forces (CAF) components (Regular Force (RegF); Reserve Force (ResF)) with an Afghanistan deployment and to assess the contribution of both component and deployment experiences to MHP using covariate-adjusted prevalence difference estimates. Additionally, mental health services use (MHSU) was descriptively assessed among those with a mental disorder. DESIGN: Data came from the 2013 CAF Mental Health Survey, a cross-sectional survey of serving personnel (n=72 629). Analyses were limited to those with an Afghanistan deployment (population n=35 311; sampled n=4854). Logistic regression compared MHP between RegF and ResF members. Covariate-adjusted prevalence differences were computed. PRIMARY OUTCOME MEASURE: The primary outcomes were MHP, past-year mental disorders, identified using the WHO's Composite International Diagnostic Interview, and past-year suicide ideation. RESULTS: ResF personnel were less likely to be identified with a past-year anxiety disorder (adjusted OR (AOR)=0.72 (95% CI 0.58 to 0.90)), specifically both generalised anxiety disorder and panic disorder, but more likely to be identified with a past-year alcohol abuse disorder (AOR=1.63 (95% CI 1.04 to 2.58)). The magnitude of the covariate-adjusted disorder prevalence differences for component was highest for the any anxiety disorder outcome, 2.8% (95% CI 1.0 to 4.6); lower for ResF. All but one deployment-related experience variable had some association with MHP. The 'ever felt responsible for the death of a Canadian or ally personnel' experience had the strongest association with MHP; its estimated covariate-adjusted disorder prevalence difference was highest for the any (of the six measured) mental disorder outcome (11.2% (95% CI 6.6 to 15.9)). Additionally, ResF reported less past-year MHSU and more past-year civilian MHSU. CONCLUSIONS: Past-year MHP differences were identified between components. Our findings suggest that although deployment-related experiences were highly associated with MHP, these only partially accounted for MHP differences between components. Additional research is needed to further investigate MHSU differences between components.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».