Mental Health and Illness Perceptions and Experiences in Canadian Christian Congregations
Bibliographic record
Abstract
Few sociological studies have explored mental health and Christian congregations. Such research is absent in Canada. Using questionnaire data with 969 Christian congregants and leaders across theological traditions in Canada, this study examines how mental health is normalized or stigmatized in Canadian congregations. We draw from symbolic interactionist theory to argue that the narratives that congregations use and the resources they draw upon to discuss and respond to mental health shape congregants’ perceptions and experiences of mental health, illness, and challenges. Our research shows that mental health and illness is both normalized and stigmatized in Canadian congregations. For instance, 67 percent ( n = 637) of respondents say they would not be embarrassed if other congregants knew they were experiencing mental health challenges, while 28 percent ( n = 267) report they would feel embarrassed. Yet congregations that embrace religious-only or absent narratives are more likely to have congregants who perceive or experience mental health stigma and less likely to seek church-based mental health support or to report church supports helped them versus congregations that incorporate some combination of a bio/psycho/social approach. We highlight opportunities for more comprehensive mental health supports along with strengthened equipping for churches in responding to mental health and illness.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".