Demographics of (non)religious groups.
Bibliographic record
Abstract
Although the threat and uncertainty of the COVID-19 pandemic has become a significant source of distress, using religion to cope may be associated with more positive health. Given the severity and chronicity of the pandemic, religious individuals may also have relied on a variety of non-religious coping methods. Much of the existing COVID-19 research overlooks the role of religious group membership and beliefs in relation to coping responses and associated mental health, with an additional lack of such research within the Canadian context. Thus, this cross-sectional study investigated relations among religiosity, stressor appraisals, (both religious and non-religious) coping strategies, mental and physical health in a religiously-diverse Canadian community sample (N = 280) during the pandemic’s 2nd wave from March to June 2021. Numerous differences were apparent in appraisal-coping methods and health across five (non)religious groups (i.e., Atheists, Agnostics, “Spiritual but not religious”, Christians, and those considered to be religious “Minorities” in Canada). Religiosity was also associated with better mental health, appraisals of the pandemic as a challenge from which one might learn or grow, and a greater reliance on problem-focused, emotional-engagement, and religious coping. Moreover, both problem-focused and emotional-engagement coping mediated the relations between religiosity and health. Taken together, this research has implications for individual-level coping as well as informing culturally-sensitive public health messages promoting targeted self-care recommendations with integrated religious or spiritual elements during times of threat and uncertainty, such as the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".