Does Religiosity Buffer the Adverse Mental Health Effects of Work-Family Strain? Examining the Role of an Overlooked Resource
Bibliographic record
Abstract
North American employees face substantial challenges in managing their work and family lives. Drawing from Hobfoll’s 2001 conservation of resources (COR) theory, work-family scholars have argued that some resources can be effective in buffering conflict in the work-family interface. We analyze data from a national sample of Canadian workers ( N = 3,431) to assess how two components of religion/spirituality—religious attendance and divine control—buffer the mental health effects of work-to-family conflict (WFC) and family-to-work conflict (FWC). Results suggest that both work-to-family conflict and family-to-work conflict were associated with higher levels of psychological distress. Our results further reveal that religious attendance buffered the pernicious effects of both WFC and FWC for psychological distress, while divine control only buffered the effects of FWC. These patterns did not appear to differ by gender. Given increasing rates of work-family strain in the North American context, out findings call for a broadening of the literature on the work-family interface, one that takes into consideration the overlooked role of religion and spirituality.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".