The Impact of the COVID-19 Pandemic on the Psychological Well-Being of Catholic Priests in Canada
Bibliographic record
Abstract
Among the general population, frontline workers have been identified to be at heightened risk for negative mental health consequences related to the COVID-19 Pandemic. Catholic priests, who minister to approximately 30% of Canadians, in their role as frontline workers, have been profoundly limited in the provision of pastoral care due to public health restrictions. However, little is known about the impact pandemic distress has on this largely understudied population. Four hundred and eleven Catholic priests across Canada participated in an online survey during May and June 2021. Multiple regression analysis examined how depression, anxiety, traumatic impact of events, loneliness, and religious coping style affect the psychological well-being, satisfaction as a priest, and priestly identity of participants. Results demonstrated that pandemic distress significantly impacts the psychological well-being of priest participants. Depression and loneliness surfaced as significant considerations associated with lowered psychological well-being. While neither anxiety nor traumatic distress reached a significance threshold, the religious coping style of participants emerged as an important factor in the psychological well-being of priests. Results of the study contribute to the understanding of how the pandemic has impacted a less visible group of frontline workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 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".