Fortifying the Front Lines: Incorporating the Buddhadharma of Suffering and Compassion into the Provision of Spiritual Care to Front-Line Hospital Staff
Bibliographic record
Abstract
This thesis explores how spiritual care providers of any faith tradition can employ the Buddhadharma—the teachings of the Buddha—to support front-line hospital staff who experience profound emotional, spiritual, and moral suffering in their work. Set within the context of a Catholic Level I Trauma Centre in Toronto during and after the COVID-19 pandemic, the research examines how spiritual care providers can address the crises of meaning, moral injury, and psychological distress that healthcare workers face daily. The study employs a hermeneutical phenomenology methodology to capture the lived experiences of ten front-line staff members through semi-structured interviews and validated psychological and spiritual surveys. Participants described a deep and often silent suffering, stemming from moral injury, lack of institutional and emotional support, and the ethical complexity of patient care, during and after the COVID-19 pandemic. The data reveal common threads, including institutional neglect, emotional exhaustion, and a longing for meaning and recognition. This suffering is interpreted through both Western philosophical frameworks (such as Simone Weil and Dorothee Söelle’s reflections on affliction) and Buddhist concepts, particularly the Four Noble Truths and the Brahmavihārās—the Four Immeasurables. The research shows that chaplaincy interpreted through a Buddhist lens, with its focus on mindfulness, compassion, and the transformation of suffering, offers a powerful and non-theistic framework for spiritual care that resonates with both religious and secular caregivers. In response to participant feedback, the second phase of the study involved the creation of three asynchronous educational video modules on Buddhist perspectives on suffering, mindfulness, and self-compassion. These modules were designed to provide staff with practical and accessible tools for personal reflection and emotional resilience. The findings suggest that a chaplaincy practice that employs this Buddhist perspective offers valuable resources for supporting staff well-being in acute care settings, especially in moments of crisis. The study concludes with recommendations for institutional investment in chaplaincy services that include spiritual care for staff, the integration of meaning-centered approaches, and the need for systemic acknowledgment of healthcare workers’ moral and emotional burdens.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| 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".