Roads to Health in Developing Countries: Understanding the Intersection of Culture and Healing
Bibliographic record
Abstract
BACKGROUND: The most important attribute to which all human beings aspire is good health because it enables us to undertake different forms of activities of daily living. The emergence of scientific knowledge in Western societies has enabled scientists to explore and define several parameters of health by drawing boundaries around factors that are known to influence the attainment of good health. For example, the World Health Organization defined health by taking physical and psychological factors into consideration. Their definition of health also included a caveat that says, "not merely the absence of sickness." This definition has guided scientists and health care providers in the Western world in the development of health care programs in non-Western societies. OBJECTIVE: However, ethnomedical beliefs about the cause(s) of illness have given rise to alternative theories of health, sickness, and treatment approaches in the developing world. Thus, there is another side to the story. METHOD: Much of the population in developing countries lives in rural settings where the knowledge of health, sickness, and care has evolved over centuries of practice and experience. The definition of health in these settings tends to orient toward cultural beliefs, traditional practices, and social relationships. Invariably, whereas biomedicine is the dominant medical system in Western societies, traditional medicine-or ethnomedicine-is often the first port of call for patients in developing countries. RESULTS: The 2 medical systems represent, and are influenced by, the cultural environment in which they exist. On one hand, biomedicine is very effective in the treatment of objective, measurable disease conditions. On the other hand, ethnomedicine is effective in the management of illness conditions or the experience of disease states. Nevertheless, an attempt to supplant 1 system of care with another from a different cultural environment could pose enormous challenges in non-Western societies. CONCLUSION: In general, we, as human beings, are guided in our health care decisions by past experiences, family and friends, social networks, cultural beliefs, customs, tradition, professional knowledge, and intuition. No medical system has been shown to address all of these elements; hence, the need for collaboration, acceptance, and partnership between all systems of care in cultural communities. In developing countries, the roads to health are incomplete without an examination of the intersection of culture and healing. Perhaps mutual exclusiveness rather inclusiveness of these 2 dominant health systems is the greatest obstacle to health in developing countries.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.049 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".