Unequal health I: determinants and regional examples
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».