Private complementary medicine and older people: service use and user empowerment
Bibliographic record
Abstract
Increasing numbers of people are using complementary therapies, and many are in older age groups. Although recent empirical research has considered the demands for complementary medicine, and in particular its recent consumer boom, little acknowledgement is given in research to the different social categories of users, and to the intricacies of their different motivations and consumer behaviours. In the context of a relative paucity of dedicated research investigations, the paper highlights the relevance of social gerontological perspectives. Based on a questionnaire survey of 144 older users, and in-depth interviews with 20 older users in southern England, it considers trends in local use and user actions, attitudes and opinions. From a disciplinary perspective, the paper also contributes to a growing body of research which focuses on older peoples’ self-implemented health and health care strategies. There is a substantial degree of user satisfaction with the therapies used, and many respondents claimed to have benefited in terms of their physical and mental health. Older users are also empowered by their treatment decisions and negotiations, many of which are made either independently, or jointly between themselves and their therapists. Treatment solutions are typically constructed with combinations of complementary therapies, or combinations of complementary therapies and orthodox health services.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".