Using Internet-Based Psychological Measurement to Capture the Deteriorating Community Mental Health Profile During COVID-19: Observational Study
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Abstract
BACKGROUND: The coronavirus disease (COVID-19) is expected to have widespread and pervasive implications for mental health in terms of deteriorating outcomes and increased health service use, leading to calls for empirical research on mental health during the pandemic. Internet-based psychological measurement can play an important role in collecting imperative data, assisting to guide evidence-based decision making in practice and policy, and subsequently facilitating immediate reporting of measurement results to participants. OBJECTIVE: The aim of this study is to use an internet-based mental health measurement platform to compare the mental health profile of community members during COVID-19 with community members assessed before the pandemic. METHODS: This study uses an internet-based self-assessment tool to collect data on psychological distress, mental well-being, and resilience in community cohorts during (n=673) and prior to the pandemic (two cohorts, n=1264 and n=340). RESULTS: Our findings demonstrate significantly worse outcomes on all mental health measures for participants measured during COVID-19 compared to those measured before (P<.001 for all outcomes, effect sizes ranging between Cohen d=0.32 to Cohen d=0.81. Participants who demonstrated problematic scores for at least one of the mental health outcomes increased from 58% (n=197/340) before COVID-19 to 79% (n=532/673) during COVID-19, leading to only 21% (n=141) of measured participants displaying good mental health during the pandemic. CONCLUSIONS: The results clearly demonstrate deterioration in mental health outcomes during COVID-19. Although further research is needed, our findings support the serious mental health implications of the pandemic and highlight the utility of internet-based data collection tools in providing evidence to innovate and strengthen practice and policy during and after the pandemic.
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Various studies reported a deterioration of people's mental health.
“Various studies instead reported a deterioration of people’s mental health [5,6,98,99].”
From Positive Impact of Mindfulness Meditation on Mental Health of Female Teachers during the COVID-19 Outbreak in Italy (2020) · cites this paper as [98]
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Additional file 1 of COVID-19 and mental health in Australia – a scoping review
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Additional file 2 of COVID-19 and mental health in Australia – a scoping review
Zhao, Y., Leach, L., Walsh, E., Batterham, P. J., Calear, A. L., Phillips, C., Olsen, A., Doan, T., LaBond, C., & Banwell, C. (2022) · Figshare · doi:10.6084/m9.figshare.20076573
- Authors
- van Agteren, J., Bartholomaeus, J., Fassnacht, D. B., Iasiello, M., Ali, K., Lo, L., & Kyrios, M.
- Affiliations
- South Australian Health and Medical Research Institute (AU)
- Flinders University (AU)
- ORCID
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2020-06-11
- Volume
- 7
- Issue
- 6
- Pages
- e20696
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC7294997
- OpenAlex
- W3028983165
- Cited by
- 66
- References
- 39
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 8 · 2022: 18 · 2021: 25 · 2020: 12
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Clinical Psychology › COVID-19 and Mental Health
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Maternal Mental Health During Pregnancy and Postpartum
- Keywords
- COVID-19 pandemic, community mental health, psychological distress, psychological well-being, resilience, mental health deterioration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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