Assessing Resilience and Its Correlates among Residents of Fort McMurray during the COVID-19 Pandemic
Notice bibliographique
Résumé
BACKGROUND: The coronavirus disease of 2019 (COVID-19) pandemic has led to a global health crisis that has affected the psychological well-being of individuals across the world. The persistence of the pandemic and measures to curtail it have tested people's ability to cope successfully and bounce back from the pandemic, otherwise referred to as resilience. The present study examined resilience levels among residents of Fort McMurray and identified the demographic, clinical and social factors associated with resilience. METHODS: The study used a cross-sectional survey design and collected data from 186 participants using online questionnaires. The survey included questions assessing sociodemographic information, mental health history and COVID-19-related variables. The main study outcome was resilience measured using the six-item Brief Resilience Scale (BRS). The data from the survey were analyzed using chi-squared tests and binary logistic regression analyses in the Statistical Package for Social Sciences (SPSS), version 25. RESULTS: The results showed that seven independent variables (age, history of depression, history of anxiety, willingness to receive mental health counselling, support from the government of Alberta and support from employer) were statistically significant within the context of the logistic regression model. A history of an anxiety disorder was demonstrated to best predict low resilience. Participants who had a history of anxiety disorder were five times more likely to show low resilience compared to those without such a history. Participants with a history of depression showed a three-fold likelihood of having low resilience in comparison to those who did not have a history of depression. Individuals who expressed a desire to receive mental health counselling had a four-times likelihood of having low resilience than those who did not express a desire to receive mental health counselling. The results also showed that younger participants were more prone to low resilience compared to older participants. Receiving support from the government and one's employer is a protective factor. CONCLUSIONS: This study highlights the importance of examining resilience and its associated factors during a pandemic such as COVID-19. The results demonstrated that a history of anxiety disorder, depression and being younger were important predictors of low resilience. Responders who reported the desire to receive mental health counselling also reported expressing low resilience. These findings could be used to design and implement interventions aimed at improving the resilience of individuals affected by the COVID-19 pandemic.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 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) | 0,001 | 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 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 ».