Othering and Being Othered in the Context of Health Care Services
Bibliographic record
Abstract
Othering is a process that identifies those that are thought to be different from oneself or the mainstream, and it can reinforce and reproduce positions of domination and subordination. Although there are theoretical and conceptual treatments of othering in the literature, researchers lack sufficient examples of othering practices that influence the interactions between patients and health care providers. The purpose of this study was to explore the interactions between health care providers and South Asian immigrant women to describe othering practices and their effects. Ethnographic methods were used involving in-depth interviews and focus group discussions. The analysis entailed identifying uses of othering and exploring the dynamics through which this process took place. Women shared stories of how discriminatory treatment was experienced. The interviews with health care professionals provided examples of how views of South Asian women shaped the way health care services were provided. Three forms of othering were found in informants' descriptions of their problematic health care encounters: essentializing explanations, culturalist explanations, and racializing explanations. Women's stories illustrated ways of coping and managing othering experiences. The analysis also revealed how individual interactions are influenced by the social and institutional contexts that create conditions for othering practices. To foster safe and effective health care interactions, those in power must continue to unmask othering practices and transform health care environments to support truly equitable health care.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".