Evidence map›Paper›PMID 42433218›Full record

ArticleFrontiers in psychiatry2026

Machine learning clustering of psychological response trajectories across the first and second waves of the COVID-19 pandemic.

Fardous Hasan, Farzana Haque, Roberto Ariel Abeldaño Zuñiga, Nourhan M Aly, Moréniké Oluwátóyìn Foláyan

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Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Fardous HasanDepartment of Information Science and Media Studies, University of Bergen, Bergen, Norway.
Farzana HaqueDepartment of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
Roberto Ariel Abeldaño ZuñigaCOVID-19 mental health and wellness (MEHEWE) Study Group, Obafemi Awolowo University, Ile-Ife, Nigeria.
Nourhan M AlyDepartment of Pediatric Dentistry and Dental Public Health, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.
Moréniké Oluwátóyìn FoláyanCOVID-19 mental health and wellness (MEHEWE) Study Group, Obafemi Awolowo University, Ile-Ife, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The COVID-19 pandemic disrupted daily life globally, leading to increased psychological distress. While many studies have documented mental health trends, few have tracked how person-centered psychological clusters emerge and evolve across pandemic waves. Methods: This longitudinal study assessed 338 adults (mean age, 38.3 years; 55% female) from 53 countries at two pandemic waves (2020 and 2021). Post-traumatic stress symptoms (PTSS; 15-item scale, α = 0.93), coping strategies (11 binary items), and pandemic stress (15 binary items) were measured using validated instruments; composite scores were constructed by summing item responses. Machine learning clustering (K-means and Gaussian mixture models) evaluated solutions Results: Three statistically defined clusters emerged at both waves: a low-distress, high-coping cluster (labeled "resilient"; Wave 1: 35.5%; Wave 2: 29.00%); a low-coping cluster (40.04%; 49.11%); and a high-distress cluster (25.06%; 21.89%). Overall, 55.3% (95% CI [50.0%, 60.5%]) remained in the same cluster across waves. Among those initially in the resilient cluster, 55.8% transitioned to higher-distress clusters by Wave 2. The low-coping cluster showed the greatest stability (68.9%), and 50.6% of those initially in the high-distress cluster transitioned to lower-distress clusters. Net flows shifted toward the low-coping cluster (31 participants). Country income level was associated with cluster membership at both waves ( Conclusion: Psychological responses to prolonged crises are dynamic and heterogeneous. Coping capacity and socioeconomic factors are associated with cluster membership, but causal inferences cannot be drawn due to the observational two-wave design and convenience sampling. These findings suggest that interventions could benefit from strengthening coping skills while also addressing structural factors such as education and employment, though further research with more frequent assessments and experimental designs is needed.

Indexed as

copingCOVID-19longitudinal studymachine learningmental healthpsychological clusters

Identifiers

PMID42433218
PMCPMC13350411

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.