How ‘alternative’ is CAM? Rethinking conventional dichotomies between biomedicine and complementary/alternative medicine
Bibliographic record
Abstract
The aim of this article is to interrogate the pervasive dichotomization of 'conventional' and 'alternative' therapies in popular, academic and medical literature. Specifically, I rethink the concepts such as holism, vitalism, spirituality, natural healing and individual responsibility for health care as taken-for-granted alternative ideologies. I explore how these ideologies are not necessarily 'alternative', but integral to the practice of clinical medicine as well as socially and culturally dominant values, norms and practices related to health and health care in Canada and elsewhere. These reflections address both theoretical and applied concerns central to the study of integration of different medical practices in western industrialized nations such as Canada. Overall, in examining homologies present in both biomedicine and complementary/alternative medicine (CAM), this article rethinks major social practices against binary oppositions by illustrating through literature review that the biomedical and CAM models may be homologous in their original inceptions and in recent cross-fertilizations towards a rigorous approach in medicine. By highlighting biomedicine and CAM as homologous symbolic systems, this article also sheds light on the potential for enhancing dialogue between diverse perspectives to facilitate an integrative health care system that meets multiple consumer needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.097 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".