Shifting paradigms: A collective and structural strategy for addressing healthcare inequity
Bibliographic record
Abstract
Healthcare inequity is a persistent systemic problem, yet many solutions have historically focused on "debiasing" individuals. Individualistic strategies fit within a competency-based medical education and assessment paradigm, whereby professional values of social accountability, patient safety, and healthcare equity are linked to an individual clinician's competence. Unfortunately, efforts to realise the conceptual linkages between medical education curricula and goals to improve healthcare equity fail to address the institutional values, policies, and practices that enable structural racism. In this article, we explore alternative approaches that target collective and structural causes of health inequity. We first describe the structural basis of healthcare inequity by identifying the ways in which institutional culture, power and privilege erode patient-centred care and contribute to epistemic injustice. We then outline some reasons that stereotypes, which are a culturally supported foundation for discrimination, bias and racism in healthcare, cannot be modified effectively through individualistic strategies or education curricula. Finally, we propose a model that centres shared values for leadership by individuals and institutions with consistency in goal setting, knowledge translation, and talent development. Figure 1 summarises the key recommendations. We have provided cases to supplement this work and facilitate discussion about the model's application to practice.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".