(Re)producing “Whiteness” in Health Care: A Spatial Analysis of the Critical Literature on the Integration of Internationally Educated Health Care Professionals in the Canadian Workforce
Bibliographic record
Abstract
PURPOSE: There is a gap in the literature to understand how professionalizing systems intersect with socioeconomic and political realities such as globalization to (re)produce social inequities between those trained locally and those trained abroad. In this critical review, the question of how systemic racism is reproduced in health care is addressed. METHOD: Electronic databases and nontraditional avenues for searching literature such as reference chaining and discussions with experts were employed to build an archive of texts related to integration of internationally educated health care professionals (IEHPs) into the workforce. Data related to workplace racialization were sought out, particularly those that used antiracist and postcolonial approaches. Rather than an exhaustive summary of the data, a critical review contributes to theory building and a spatial analysis was overlayed on the critical literature of IEHP integration to conceptualize the material effects of the convergence of globalization and professional systems. RESULTS: The critical review suggests that professions maintain their value and social status through discourses of "Canadianness" that maintain the homogeneity of professional spaces through social closure mechanisms of credential nonrecognition and resocialization. Power relations are maintained through mechanisms of workplace racialization/spatialization and surveillance which operate through discourses of "foreign-trainedness." CONCLUSIONS: Movement of professionals supports a professional system that on the surface values diversity while maintaining its social status and power through the (re)production of the discourse of "Whiteness." The analysis shows how in the process domestic graduates are emplaced as the "rightful" citizens of professional paces while IEHPs are marginalized in the workforce.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.020 | 0.048 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".