Unequal health I: determinants and regional examples
Bibliographic record
Abstract
Great and growing global inequity, the burden of poverty both absolute and relative, millions of preventable deaths every year – these unsettling features of today's world lead many students toward global development and health work because it seems like the only decent thing to do. Suri et al. (2013: 245) Brown and Taylor (2018) suggest that the study of health inequalities needs to be at the centre of geographical research on global health. It is therefore appropriate to begin an exploration of global health by describing, and explaining, the health inequality (more accurately, health inequity ) that exists between and within different countries. “Health inequality” is invariably the term in common use, and so I follow that trend, although “health inequity” conveys more clearly that differences in health may be preventable – avoidable and unjust. The issue of who or what are responsible for avoidance – human agency, or wider structures of economy and politics – is still debated, although a critical geographical perspective suggests very much the latter. On this and other issues relating to health inequalities, the paper by Arcaya, Arcaya and Subramanian (2015) is valuable. There is a vast literature on health inequalities in countries of the Global North, to which geographers have made key contributions, such as in the United States (McLafferty, Wang & Butler 2011), Canada (Shantz & Elliott 2021), the United Kingdom (Bambra 2016; Dorling 2013), New Zealand (Pearce, Tisch & Barnett 2008) and elsewhere. I do not intend to review these contributions, preferring to focus attention on some countries of the Global South. However, within some countries of the Global North there are marginalized groups to which I do want to give attention; in particular, as we see later, there are Indigenous populations occupying places that are as neglected as those who inhabit them. The Global North literature on health inequalities tends to focus both on regional inequalities and, frequently, on more local – neighbourhood – variations. A classic distinction in this literature is between compositional and contextual effects (Gatrell & Elliott 2015: 82; Brown et al . 2018: ch. 8). In other words, is poor health in specific settings determined by the fact that poor people, with health-damaging behaviours, live there (the “composition” of a place) or by the fact that people live in places (“contexts”) that are environmentally or socio-economically deprived?
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".