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Record W2937320573 · doi:10.1111/1467-9566.12886

Brosnan, C., Vuolanto, P. and Brodin Danell, J.‐A.Complementary and Alternative Medicine: Knowledge Production and Social Transformation. Palgrave Macmillan. 2018. 337pp. 2 illus. ISBN 978‐3‐319‐73939‐7

2019· article· en· W2937320573 on OpenAlexaboutno aff
Ellen Stewart

Bibliographic record

VenueSociology of Health & Illness · 2019
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChinaSummitBiomedicinePower (physics)SociologyPolitical scienceDelegationSocial sciencePublic relationsLaw

Abstract

fetched live from OpenAlex

Complementary and alternative medicine (CAM) has been a fertile topic for sociologists of health and illness for decades. It is a field often presented as centring on a conflict between proponents of CAM, and the arrayed infrastructure and agents of ‘conventional’ biomedicine. This valuable collection presents eleven studies which seek to trouble, or at least open up, that long-standing assumption. In their introductory chapter, Brosnan, Vuolanto and Brodin Danell offer an expansive, insightful review of existing literature which should become standard reading for scholars of these issues. They argue that in academic analyses CAM has operated as a ‘signifier of other, broader societal shifts’ and ‘a provocative entity … used to increase or undermine the power of patients and practitioners’ (p2). The editors then make the case for approaches which, instead, examine specific instances of CAM practices as found, avoiding easy assumptions about the homogeneity of therapies within the category. The following 11 chapters take up this challenge, exploring how CAM is assembled and especially evidenced differently through practices in diverse contexts. The editors (themselves an international team) have brought together studies of CAM in the Czech Republic, Finland, China, Brazil, Portugal, Kyrgyzstan, Sweden, Canada, Denmark and the USA. The resulting chapters are eclectic and fascinating to read. For example, Stockelova and Klepal's contribution opens with a description of a peculiar ministerial summit at which Health Ministers of Central and Eastern European countries meet a Chinese delegation promoting traditional Chinese Medicine, amid exhibits at which practitioners ‘could perform diagnosis either by examining your tongue and pulse or by connecting you to their computers’. As well as an intriguing ethnographic vignette in its own right, such description highlights one key theme of the collection, explicating how CAM is assembled transnationally. The chapter from Almeida, Siegel and De Barros enlarges on this theme, comparing how global governance of three therapies has been ‘reinterpreted locally’ in Brazil and Portugal. Another key theme evident across the collection is how evidence for CAM is constructed and mobilised in practice. Ning's ethnographic study of how Traditional Chinese Medicine practitioners employ epistemic hybridity in combining biomedical/scientific and traditional/experiential sources of evidence is an exceptionally lucid explanation of complex conceptual terrain. Elaborating differences between the perspectives of different practitioners within the study, as well as a determined commitment to the validity of different knowledge systems, permit a generous exploration of contemporary challenges in Traditional Chinese Medicine practice within Canadian integrative healthcare. The case for examining knowledge production and application in CAM scholarship is well-made in the introductory chapter, and evidenced through the subsequent chapters. The absence of a concluding chapter means that there is little sense of the collection arriving at a firm position. This is in keeping with the editors’ stated goal of taking the sociological study of CAM in a new direction; a beginning, not an ending. Yet by the end of the collection, I sometimes missed the energy of more passionate contributions to the sociology of CAM. Studies of CAM have often been explicitly positioned precisely because of histories of biomedical failure of particular patients (especially those with chronic and multiple illness) and histories of the exploitation of patients by unscrupulous ‘CAM’ practitioners. The commitment of some chapters to epistemological and ontological symmetry can feel uncomfortable in this highly normative context. For example, Penkala-Gawecka's Actor Network Theory analysis of a ‘miracle bed’ in Kyrgystan emphasises the ‘profound agency’ of the bed in stabilising the network of actors (human and non-human) studied. While I genuinely enjoyed the analysis, the images that stayed with me were instead the dispassionate descriptions of financially strapped retirees saving their small pensions or selling their last cow in the hope of acquiring the bed. In a cover quote, Wahlberg welcomes the collection's ability to ‘look past the polemics’. Undoubtedly we do need to move beyond the ‘CAM wars’ in search of greater insights about both CAM and its ‘conventional’ other: this collection ably makes that case and demonstrates the value of this progression. The challenge which remains is to do this without losing sight of continued vulnerabilities and injustices within the field.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0020.004
Scholarly communication0.0070.007
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0470.016

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.

Opus teacher head0.042
GPT teacher head0.346
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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