Mental health of informal caregivers during the COVID-19 pandemic: a Swiss cohort study
Bibliographic record
Abstract
AIMS OF THE STUDY: The COVID-19 pandemic and related public health measures have disrupted healthcare systems and may have impacted informal caregivers' mental health due to increased responsibilities and limited access to support services. This study aimed to examine the prevalence of mental distress among caregivers and non-caregivers and identify risk and protective factors for mental distress in caregivers during the COVID-19 pandemic. METHODS: Data were collected from participants in Specchio-COVID19, a population-based cohort in Geneva, Switzerland. Mental distress was measured using the 12-item General Health Questionnaire (GHQ-12) in June 2021. The prevalence of distress was compared between caregivers and non-caregivers. Risk and protective factors for mental distress among caregivers were explored using logistic regressions. RESULTS: Among the 5416 participants, 1086 (20%) reported helping someone in a non-professional manner with activities of daily life and were considered caregivers. Mental distress was more frequent in caregivers than in non-caregivers (41% vs 37%, p = 0.010). In caregivers, limited social support (adjusted odds ratio [aOR] = 1.25 [95% confidence interval: 1.10, 1.42]), caring for an individual with a mental condition (aOR = 1.21 [1.05, 1.41]), living over 10 km away from the care recipient (aOR = 1.17 [1.02, 1.34]), feeling more isolated in one's caregiving role (aOR = 1.20 [1.08, 1.32]), worrying about caregiving ability in case of COVID-19 or quarantine (aOR = 1.18 [1.08, 1.28]) and experiencing reduced availability of healthcare professionals (aOR = 1.11 [1.02, 1.22]) were associated with increased odds of mental distress. CONCLUSION: Informal caregivers experienced higher levels of mental distress than non-caregivers during the COVID-19 pandemic. This study highlights the need for public health policies that enhance both formal and informal support networks and include rapidly implementable solutions for caregiving continuity, benefiting both caregivers and their care recipients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".