‘Underclassism’ and access to healthcare in urban centres
Bibliographic record
Abstract
In this article, we draw on findings from an ethnographic study that explored experiences of healthcare access from the perspectives of Indigenous and non-Indigenous patients seeking services at the non-urgent division of an urban emergency department (ED) in Canada. Our aim is to critically examine the notion of 'underclassism' within the context of healthcare in urban centres. Specifically, we discuss some of the processes by which patients experiencing poverty and racialisation are constructed as 'underclass' patients, and how assumptions of those patients as social and economic Other (including being seen as 'drug users' and 'welfare dependents') subject them to marginalisation, discrimination, and inequitable treatment within the healthcare system. We contend that healthcare is not only a clinical space; it is also a social space in which unequal power relations along the intersecting axes of 'race' and class are negotiated. Given the largely invisible roles that healthcare plays in controlling access to resources and power for people who are marginalised, we argue that there is an urgent need to improve healthcare inequities by challenging the taken-for-granted assumption that healthcare is equally accessible for all Canadians irrespective of differences in social and economic positioning.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".