Contextualizing Integration: A Critical Social Science Approach to Integrative Health Care
Bibliographic record
Abstract
This article argues for the importance of examining the phenomenon of integrative health care in broader social and historical contexts. The authors examine mainstream approaches to identify patterns of integrative medicine and criticize them for their neglect of clashes among different philosophical paradigms and the wider social contexts that govern health care in practice. The authors outline a framework and highlight the values of a critical social science perspective in deepening our understanding of recent transformations in health care practice and issues surrounding biomedicine and complementary/alternative medicine (including chiropractic, naturopathy, massage, acupuncture/oriental medicine, etc) and traditional medicine. A critical social science perspective pays special attention to complex power relations, inclusionary/exclusionary strategies, and interprofessional dynamics in medicine. Drawing upon recent research findings, the authors illustrate how such a perspective reveals the intricacies and tensions that surround the integration of different paradigms of health care practice. The authors summarize the importance of situating integrative health care in structural contexts and affirm their commitment to a critical social science approach.
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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| 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".