The impact of the COVID-19 pandemic on mental and psychosocial functioning, quality of life, and recovery in adults with severe mental illness: Findings from Dutch longitudinal cohorts
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has impacted global mental health, with individuals with severe mental illness (SMI) being particularly vulnerable. Research on changes in psychiatric symptoms during this pandemic has yielded inconsistent results, often due to individual heterogeneity and a limited focus on broader outcomes such as psychosocial functioning, societal and personal recovery, and quality of life (QoL). Furthermore, long-term effects remain underexplored. This longitudinal cohort study aimed to assess the COVID-19 pandemic's impact on mental and psychosocial functioning, QoL, and recovery in individuals with SMI, and to explore individual and treatment characteristics associated with outcome changes. METHODS: Two cohorts were included, involving adults (≥18 years) diagnosed with DSM-5 disorders and experiencing long-term impairments. Participants received care between January 1, 2018 and December 31, 2023. Outcomes included the Health of the Nation Outcome Scales, the Manchester Short Assessment of Quality of Life, and the Individual Recovery Outcomes Counter. Changes were analyzed across five pandemic periods using linear mixed models. RESULTS: Improvements in mental and psychosocial functioning, QoL, and recovery were observed over time, regardless of the COVID-19 pandemic period. However, progress was slower during the COVID-19 pandemic compared to pre-pandemic levels. No individual or treatment characteristics were significantly linked to changes in outcomes. CONCLUSION: The findings suggest that the COVID-19 pandemic had a minimal negative impact on individuals with SMI. This may be due to the marginal negative effects of the pandemic on this population, or the mitigating role of stabilizing factors within the current Dutch care models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".