Social Justice and Health Equity: Core Ethical Values in the Health Sciences
Bibliographic record
Abstract
Abstract Social justice and health equity are frequently invoked in the health sciences. They are commonly referred to as ‘core values’ or ‘central aims’ that health research, medicine and other health interventions should seek to realise. Yet, despite their ubiquity and stature in the health sciences, it is rarely made clear exactly what these values mean and how they ought to be reflected in practice. If the ethical commitments of social justice and health equity are not articulated or are unclear when they are invoked, it is unlikely that these ‘core values’ will meaningfully guide work in the health sciences, and it is even less likely that progress will be made towards health equity and social justice. It is therefore imperative to understand the ethical foundations of these concepts, some prominent ways that they might be interpreted in the health sciences and the important differences between health equity and social justice. Social justice and health equity are frequently identified as ‘core values’ in the health sciences, yet it is often not made clear what these values should mean in practice. Because health inequities are differences in health that are considered to be ‘unjust’, clarity is required about what makes a difference in health unjust and what a ‘just’ state of population health should look like. Failing to clearly and explicitly articulate the ethical norms or standards that health equity and social justice should commit health scientists to is likely to lead to ethically dubious, inconsistent and unsound policy and practice decisions, which may in turn contribute to the creation, maintenance or exacerbation of social injustice and health inequities – the very outcomes health equity and social justice are trying to address. Broadly speaking, conceptions of justice tend to address the following dimensions: distributive justice, relational justice and procedural justice. Each of these dimensions is relevant to considerations of health equity and social justice in the health sciences. Evidence suggests that explicit consideration of the normative dimensions of health equity is not the norm. As a result, a term that superficially gestures towards ‘fairness’ and ‘social justice’ can be invoked without being forced to meaningfully engage with phenomena that are central to the concerns of social justice: racism, sexism, colonialism and other forms of oppression.
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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 | 0.086 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.135 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".