Examining Medical Anthropological Theory as a catalyst for the failure of Clinically Applied Medical Anthropology
Bibliographic record
Abstract
Medical anthropological theory may be understood in two ways: first as a set of anthropological concepts and second as the application of these concepts. The theoretical concepts themselves are rarely challenged because they have been fairly well developed. However, the approach to theory and its application has traditionally been underdeveloped and thus requires more thought and practice among anthropologists. This paper asserts that a particularly clear example of the problem with the approach to and application of medical anthropological theory can be viewed in the context of clinically applied medical anthropology (CAMA). I examine two medical anthropological concepts that applied medical anthropologists use in their dealings with clinicians – critical medical anthropology and the culture concept. In doing this, I demonstrate that although these concepts are useful and clinicians need to employ them, there are a number of problems with the theoretical approach. I argue that these problems limit the application of these concepts to CAMA and offer preliminary suggestions to resolve them. In particular, clinically applied anthropologists employing critical theory should work to present a more balanced view of the clinic and physician. In addition, anthropologists working in the clinical setting must update the CAMA literature to ensure a thorough assessment of the current use of anthropological knowledge and concepts – such as culture – in medical schools and clinics.
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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 | 0.166 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.017 | 0.149 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.015 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".