Exploring racism and racialization in the work of healthcare chaplains: a case for a critical multifaith approach
Bibliographic record
Abstract
The global COVID-19 pandemic has revealed healthcare settings as sites of much-needed scrutiny as to the workings of racism and racialization in shaping healthcare encounters, health outcomes, and workplace conditions. Little research has focused on how healthcare chaplains experience and respond to social processes of racism and racialization. We apply a critical race lens to understand racism and racialization in healthcare chaplaincy, and inspired by Patricia Hill Collins, propose a "critical multifaith approach." Drawing on research in healthcare in Canada and England, we generated four composite narratives to analyze racialization's variability and resistances employed by Indigenous, Arab, Black, and White chaplains. The composites disclose complex intersecting histories of colonialism, religion, race, and gender. Developing a critical multifaith perspective on healthcare delivery is an essential competency for chaplains wanting to impact the systems in which they serve in the direction of more equitable human flourishing.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.042 | 0.087 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| 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".