Complementary and Alternative Medicine as an Invisible Health Support Workforce
Bibliographic record
Abstract
Introduction Complementary and alternative medicine (CAM) is a complex phenomenon and difficult to define. According to the World Health Organization (WHO) (2013:15), ‘the terms “complementary medicine” or “alternative medicine” refer to a broad set of health care practices that are not part of that country's own tradition or conventional medicine and are not fully integrated into the dominant healthcare system’. The WHO (2013:7) also states that CAM ‘is an important and often underestimated part of healthcare … found in almost every country in the world and the demand for its services is increasing’. In this chapter CAM is analysed in terms of its links to health support work. From the point of view of the sociology of professions, the term CAM includes, at one end of the spectrum, statutorily regulated and professionalised therapies such as osteopathy and chiropractic in the UK which have achieved exclusionary social closure in neo-Weberian terms (Saks 2008). Further along the line are acupuncture and homeopathy, which, although not statutorily regulated, have their own voluntary self-regulatory bodies, such as the British Acupuncture Council and the British Homeopathic Association. At the other end of the spectrum are those CAM therapies less disposed to professionalisation, usually not regulated by the state, lacking consistent educational standards and with a strong emphasis on self-help, such as reiki and yoga (Saks 2008). To add to this, the status of CAM varies across countries. Traditional acupuncturists, for example, are statutorily regulated in Canada (Ijaz et al 2016), but not in the UK, despite being governed by limited local by-laws (Saks 1995). A similar conceptual ambiguity exists with respect to health support workers more generally. The latter include not only those unqualified workers who assist professionally qualified health care workers in the delivery of care, but also those who are providers of care themselves – such as the case of CAM practitioners working in the private sector directly paid by service users. As stated by Manthorpe and Martineau (2008:4– 5): … tasks performed [by support workers] will depend on the specific type of support worker under consideration and the wishes and needs of the person they support, and may range from personal care, healthcare, community participation, assistance in rehabilitation and advocacy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".