Bibliographic record
Abstract
Overview Few studies focus on the influence of religion or a religious world view with regard to health care in Africa. An impressive policy review on health care in Africa by Cooke (2009), for example, acknowledges the importance of religion but does not go beyond that point in exploring the connection. A recent and valuable study of health-care policy in Africa acknowledged the importance of spirituality, but its agenda on state and society only touched upon religious institutions (Gros 2016: 179). The intersection point of religion, health care, and Africa emerges as a principal item for the research agenda on well-being. Among other aspects of this book, the primary argument is that religious beliefs (including, by extension, the beliefs of those who do not necessarily profess religious beliefs) are central to an integrated world view. This world view shapes Africans’ understanding of the world and how best to respond to its complexity. Health care is an essential aspect of an individual's physical, emotional, and psychological well-being. The study examines the relationship of the physical and spiritual domains by examining how religious belief affects the provision and consumption of public health. States sometimes cannot fulfill their social contract to citizens. Thus, dysfunctional states create an opening for religion to impact significantly on both the provision and consumption of health care in Africa. Government corruption and incompetence vary throughout the continent, but it is fair to say that no regime in Africa is free from the side effects, which include seeking public services from the private sector. This is one underlying explanation for why traditional healers, along with Faith-Inspired Institutions that provide modern medicine, turn out to be so important in the pages of this book. The other foundational reason is the importance to Africans, in seeking health care and other services, of meeting needs in a way that is in line with their religious faith. Many Africans pursue health in a holistic way—spiritual and physical needs are regarded as connected to each other. Among Africans, the role of religion is significant across the board. The impact of religion on politics and policy in Africa is well established (Meier and Steinforth 2014). Faith-based medicine is long-standing. Some time ago, Mbiti (1969: 166, 170–171) identified traditional medicine with herbalism and, although allowing for exceptions, cast it in a generally positive light.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".