Politicizing the White Coat:
Bibliographic record
Abstract
The Canadian identity narrative typically centres on two features: universal healthcare and a longstanding tradition of welcoming newcomers – in particular, refugees. In 2012, this mythology was troubled when, without warning, asylum seekers’ healthcare access was dramatically limited. In an equally dramatic fashion, physicians and the greater healthcare community took to the streets, occupied offices, and interrupted politicians in an effort to restore refugee claimants’ access to healthcare. While this physician-led response was unprecedented in Canada, physicians had previously rallied in a similar fashion in two other universal healthcare countries: England (2003) and Germany (1993). Across all three cases, formidable physician responses emerged following efforts to remove or restrict asylum seekers’ healthcare access. In Canada, asylum seeker health restrictions, and the successful social movement they spurred were unexpected entirely. In England, attempts to restrict access are expected, but the government’s failure to implement wide-scale reforms are not. Finally, in Germany, restrictions are potentially expected, but one also expects the decades-long advocacy movement to have created national-level change; instead, ripples of impact are seen unevenly across the country. This prompts two central questions: what conditions are necessary for a national government to successfully implement restrictions on asylum seeker healthcare? And, what conditions will support physician-led social movements’ efforts to reverse these legislative changes? This thesis examines these two questions in a three-case comparison of Canada, England and Germany. Drawing on over 60 qualitative interviews with physicians, policymakers, and politicians, this study takes an ecological approach to understanding what factors facilitate reform, and what factors shape advocacy movements. In particular, this study identifies factors at each of the macro, meso, and micro-levels of analysis to map advocacy movements against their institutional contexts and political climates. By examining social movements as creatures of their policy and ideational contexts, this thesis provides a holistic examination of the people, organizations, and institutions that shape asylum seeker healthcare. This study identifies features of movements and contexts that will impact advocacy efforts; these findings are of use to scholars of social movements but also everyday advocates and persons driving change in asylum seeker social policy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.066 | 0.031 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".