Depression, anxiety and worry in young Pacific adults in New Zealand during the COVID-19 pandemic

Siegert, Richard J.; Narayanan, Ajit; Dipnall, Joanna F.; Gossage, Lisa; Wrapson, Wendy; Sumich, Alexander; Mérien, Fabrice; Berk, Michael; Paterson, Janis; Tautolo, El‐Shadan

doi:10.1177/00048674221115641

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…Given the scarcity of existing national study on the impact of COVID-19 on mental health in Fiji, we have compared our study findings with other countries. A similar finding was observed in young adults in New Zealand during the COVID-19 outbreak ( 44 ). Studies from around the world have consistently reported an increase in mental health issues including depression, anxiety, and stress during the pandemic due to various stressors such as isolation, fear of infection, financial difficulties, and loss of loved ones ( 45 , 46 ).…”

Section: Discussionsupporting

confidence: 82%

Prevalence and associated risk factors for mental health problems among young adults in Fiji Island during COVID-19: a cross-sectional study

Khan,

Patwary,

Mamum

et al. 2023

Front. Public Health

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IntroductionThe COVID-19 pandemic has had a significant impact on mental health globally. To understand the impact of the pandemic on mental health in Fiji, this study aimed to investigate the prevalence of anxiety disorder and depression among the young adults.MethodAn online survey was conducted to assess the prevalence of anxiety disorder and depression among the general population in Suva, Fiji during the COVID-19 pandemic. A total of 1,119 Fiji adults participated in the study. The study was conducted between May 20 to June 30, 2022, using a snowball sampling via social media platforms. The Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) scales were used to measure anxiety and depression, respectively. The COVID-19 related stressors was evaluated using the adapted SARS stressors assessment. Univariate and multivariate logistic regression analysis was performed to determine the factors influencing mental health among respondents.ResultsThe result shows that a significant portion of individuals experienced each of the stressors, with the highest prevalence seen for hearing information about the severity of COVID-19. The prevalence of anxiety and depression was found to be 45% and 49%, respectively. Being female, having pre-existing illness and COVID-19 stressors were a risk factor to develop anxiety and depression. On the other hand, employed individuals and having high BMI was a protective factor against developing depression during COVID-19 lockdown.ConclusionThese findings highlight the importance of addressing the mental health needs of the Fijian population during the COVID-19 pandemic and beyond.

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Section: Discussionsupporting

confidence: 82%

Prevalence and associated risk factors for mental health problems among young adults in Fiji Island during COVID-19: a cross-sectional study

Khan,

Patwary,

Mamum

et al. 2023

Front. Public Health

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Adolescents’ perceived stress of COVID-19 and self-compassion in Saudi Arabia: A cross-sectional study

Hammad

Alzhrani

Almulla

2023

International Journal of Nursing Sciences

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Evaluating the Efficacy of AI-Based Interactive Assessments Using Large Language Models for Depression Screening

Jin,

Bi,

et al. 2024

Preprint

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The evolution of language models, particularly the development of Large Language Models like ChatGPT, has opened new avenues for psychological assessment, potentially revolutionizing the rating scale methods that have been used for over a century. This study introduces a new Automated Assessment Paradigm (AAP), which aims to integrate natural language processing (NLP) techniques with traditional measurement methods. This integration enhances the accuracy and depth of mental health evaluations, while also addressing the acceptance and subjective experience of participants—areas that have not been extensively measured before. A pilot study was conducted with 32 participants, seven of whom were diagnosed with depression by licensed psychiatrists using the Clinical Interview Schedule-Revised (CIS-R). The participants completed the BDI-Fast Screen (BDI-FS) using a custom ChatGPT (GPTs) interface and the Chinese version of the PHQ-9 in a private setting. Following these assessments, participants also completed the Subjective Evaluation Scale. Spearman’s correlation analysis showed a high correlation between the total scores of the PHQ-9 and the BSI-FS-GPTs. The agreement of diagnoses between the two measures, as measured by Cohen’s kappa, was also significant. BSI-FS-GPTs diagnosis showed significantly higher agreement with the current diagnosis of depression. However, given the limited sample size of the pilot study, the AUC value of 1.00 and a sensitivity of 0.80 at a cutoff of 0.5, with zero false positive rate, likely overstate the classifier’s performance. Bayesian factors suggest that participants may feel more comfortable expressing their true feelings and opinions through this method. For ongoing follow-up research, a total sample size of approximately 104 participants, including about 26 diagnosed individuals, may be required to ensure the analysis maintains a necessary power of 0.80 and an alpha level of 0.05. Nonetheless, these findings provide a promising foundation for the ongoing validation of the new AAP in larger-scale studies, aiming to confirm its validity and reliability.

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Depression, anxiety and worry in young Pacific adults in New Zealand during the COVID-19 pandemic

Cited by 3 publications

References 40 publications

Prevalence and associated risk factors for mental health problems among young adults in Fiji Island during COVID-19: a cross-sectional study

Prevalence and associated risk factors for mental health problems among young adults in Fiji Island during COVID-19: a cross-sectional study

Adolescents’ perceived stress of COVID-19 and self-compassion in Saudi Arabia: A cross-sectional study

Evaluating the Efficacy of AI-Based Interactive Assessments Using Large Language Models for Depression Screening

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