The prevalence and correlates of low resilience in patients prior to discharge from acute psychiatric units in Alberta, Canada
Notice bibliographique
Résumé
BACKGROUND: Many people experience at least one traumatic event in their lifetime. Although such traumatic events can precipitate psychiatric disorders, many individuals exhibit high resilience by adapting to such events with little disruption or may recover their baseline level of functioning after a transient symptomatic period. Low levels of resilience are under-explored, and this study investigates the prevalence and correlates of low resilience in patients before discharge from psychiatric acute care facilities. METHODS: Respondents for this study were recruited from nine psychiatric in-patient units across Alberta. Demographic and clinical information were collected via a REDCap online survey. The brief resilience scale (BRS) was used to measure levels of resilience where a score of less than 3.0 was indicative of low resilience. A chi-square analysis followed by a binary logistic regression model was employed to identify significant predictors of low resilience. RESULTS: A total of 1,004 individuals took part in this study. Of these 35.9% were less than 25 years old, 34.7% were above 40 years old, 54.8% were female, and 62.3% self-identified as Caucasian. The prevalence of low resilience in the study cohort was 55.3%. Respondents who identified as females were one and a half times more likely to show low resilience (OR = 1.564; 95% C.I. = 1.79-2.10), while individuals with 'other gender' identity were three and a half times more likely to evidence low resilience (OR = 3.646; 95% C.I. = 1.36-9.71) compared to males. Similarly, Caucasians were two and one-and-a-half times respectively more likely to present with low resilience compared with respondents who identified as Black (OR = 2.21; 95% C.I. = 1.45-3.70) or Asian (OR = 1.589; 95% C.I. = 1.45-2.44). Additionally, individuals with a diagnosis of depression were significantly more likely to have low resilience than those with a diagnosis of either bipolar disorder (OR = 2.567; 95% C.I. = 1.72-3.85) or schizophrenia (OR = 4.081;95% C.I. = 2.63-6.25). CONCLUSION: Several demographic and clinical factors were identified as predictors of likely low resilience. The findings may facilitate the identification of vulnerable groups to enable their increased access to support programs that may enhance resilience. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov, NCT05133726. Registered on the 24th of November 2021.
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 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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».