Navigating Faith in Clinical Practice: A Qualitative Study of Mental Health Professionals Working with Immigrant Clients
Bibliographic record
Abstract
In Canada's multicultural society, faith plays a vital role in the lives of many immigrants. However, mental health professionals (MHPs) often overlook or disregard immigrant clients' spiritual and religious beliefs, despite their potential impact on mental health and coping mechanisms. To address this gap, this qualitative study sought to explore how MHPs navigate faith in clinical practice through interviews with 10 MHPs in Alberta, Canada, yielding eight core themes: Conceptualization of Faith, Strategies for Incorporating Faith into Practice, Fostering Strong Client Relationships, Faith Informing Practice and Professional Growth, Faith as a Salient Dimension of Mental Health, Faith Competence in Multicultural Practice for Client-Centered Care, Pathways for Faith-Based Training and Learning, and Barriers to Integrating Faith. The study infuses scholarship on the intersections between clinical practice and faith, providing insights into the real-life experiences of MHPs. The discussion examines two key areas: building faith competence in mental health care and overcoming barriers in faith-centered care. It also explores the implications of faith competence on theory, practice, and policymaking. The research’s limitations, strengths, and future directions are highlighted. In conclusion, this study underscores the need for faith competence in mental health care, promoting inclusive practice that honors diverse beliefs and experiences.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".