Keeping the Faith: Religion, Positive Coping, and Mental Health of Caregivers During COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has resulted in major stressors such as unemployment, financial insecurity, sickness, separation from family members, and isolation for much of the world population. These stressors have been linked to mental health difficulties for parents and caregivers. Religion and spirituality (R/S), on the other hand, is often viewed as promotive of mental health. However, the mechanisms by which R/S might promote mental health for parents during the pandemic remain unclear. Thus, this longitudinal study explores how R/S is associated with better caregiver mental health during the COVID-19 pandemic through higher levels of positive coping skills. A sample of N = 549 caregivers (parents and other adults in childrearing roles) across Canada, the United States, the United Kingdom, and Australia were recruited through the Prolific ® research panel [67.8% female; age M = 41.33 years ( SD = 6.33), 72.3% White/European]. Participants were assessed on measures of psychological distress, coping, R/S, and COVID-19 disruption at three time points between May and November 2020. Cross-lagged panel analysis revealed that caregiver coping mediated the relationship between caregiver R/S and caregiver mental health. Findings highlight a mechanism through which R/S naturally conveys a mental health benefit during periods of social disruption, which may provide an important target for public health promotion and clinical intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".