Small business complementary medicine: a profile of British therapists and their pathways to practice
Bibliographic record
Abstract
During recent years, private sector complementary medicine has grown as a significant provider of health care in the UK, and according to many current definitions, this provision may be classified as primary health care. In the context of a relative paucity of dedicated research investigations, and through a combined questionnaire (n = 426) and interview survey (n = 49), this paper provides some base-line evidence on the national sector, considers the previous employment of private therapists, their reasons for practicing, the range of modalities practiced and the positives they gain from this form of caring and business ownership. Although some younger therapists had moved straight from their education into private practice, the most common scenario was for middle-aged persons to enter the sector directly from skilled professional jobs and hence had radically changed their careers. Many were formerly employed in caring-related professions such as nursing, social work and teaching. Often therapists had been disillusioned with particular aspects of their former jobs. However, more frequently, they were simply attracted by the therapies themselves, the conceptual paradigms which underpin them and to the different experiences of business ownership and caring practices. Little is known about private complementary medicine, and it is argued that dedicated studies could build the initial evidence presented here, particularly within the rubric of primary health care research.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".