Successful Societies: How Institutions and Culture Affect Health (review)
Bibliographic record
Abstract
Reviewed by: Successful Societies: How Institutions and Culture Affect Health Lindsay McLaren Successful Societies: How Institutions and Culture Affect Health edited by Peter A. Hall and Michèle Lamont. New York: Cambridge University Press, 2009. This book is the product of the “successful societies” program at the Canadian Institute for Advanced [End Page 404] Research (CIFAR), which comprises a diverse group of social scientists (sociology, political science, history, epidemiology, and psychology) brought together by CIFAR to “think about what defines successful societies and the social conditions that sustain them.” This same organization (formerly known by the acronym CIAR) produced Why Are Some People Healthy and Others Not (Evans, Barer, and Marmor 1994), which was foundational to the population health movement in Canada and internationally. Though the two books are grounded in some of the same empirical observations (e.g., the social gradient in health), Successful Societies effectively builds on the earlier work by incorporating different but complementary disciplinary perspectives and emphases. The stated cross-cutting theme of Successful Societies is to explore the role of institutions and culture in producing (or detracting from) population health. “Institutions and culture” include a range of societal attributes such as administrative structures, social and economic models and programs, and cultural imaginaries (norms), which powerfully influence social relationships/resources and therefore health. Building on Sen’s capabilities theory, health (which is a function of the “wear and tear of daily life”) is viewed as dependent on one’s capacity to effectively negotiate life challenges. This capacity reflects not just material resources but also social resources (e.g., the ability to secure cooperation from others). These resources are not perfectly coupled, as suggested by the increasing number of outliers on the health-wealth graph (i.e., countries for which population health is better/worse than would be expected based on economic circumstances alone; see Evans, Chapter 4). Although the idea that population health is more than economic circumstances is not new (e.g., see Wilkinson and Pickett 2009; World Health Organization 2008), the book effectively exploits diverse “case examples” to explore the role of institutions and culture in explaining such exceptions. For example, Swidler (Chapter 5) contrasts AIDS-related initiatives in Botswana where, despite wealth and capable government, efforts have largely failed; and Uganda, where success in prevention and treatment was achieved despite seemingly unfavourable economic and administrative conditions. The unexpected outcomes are seen as reflecting differences in the match or fit (strong in Uganda; weak in Botswana) between the prevention/treatment initiative—especially the way in which it was implemented—and the existing cultural norms and institutional frameworks. A related theme emphasized throughout the book is the importance of historical/comparative approaches for understanding the determinants of population health, which may be “macro” and “slow-moving” (Hertzman and Siddiqi, Chapter 1); for example, the cultural and institutional attributes of wealthy countries with steep versus shallow social gradients in health, over time periods (eras) bounded by definable events (e.g., the Quiet Revolution in Quebec, discussed by Bouchard in Chapter 7; World War II; or the fall of communism in Europe). This approach has clear research implications: the need for appropriate (long-term, comparable) data and analytic/interpretive tools from a range of disciplines. Certain chapters stand out. The book’s introductory chapter by Hall and Lamont is especially rich, which in some cases undermines subsequent chapters because the main point or best example from the chapter is provided in the introduction. Jenson (Chapter 8) tackles the important question of why—even in the face of scientific consensus—policy change does not always occur. Using as a case example the sanitary reforms in Victorian England, Jenson illustrates how the citizenship regime and its constituent elements (e.g., beliefs about the responsibilities of state, market, families; definitions, both formal and informal, of citizenship) shed light on the implementation and impact of health initiatives. During this era, despite relative scientific consensus about the need to ensure clean water, clean streets, and adequate housing, full implementation of these [End Page 405] standards was impossible due to a citizenship regime dominated by a belief in the prominent role of the market for provision of services. There was no incentive, however...
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".