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Record W1979410277 · doi:10.1017/s0144686x02008668

Private complementary medicine and older people: service use and user empowerment

2002· article· en· W1979410277 on OpenAlexaff
Gavin J. Andrews

Bibliographic record

VenueAgeing and Society · 2002
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmpowermentAcknowledgementContext (archaeology)NaturopathyPsychologyRelevance (law)Mental healthPublic relationsMedicineAlternative medicinePolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.310
Teacher spread0.252 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations69
Published2002
Admission routes1
Has abstractyes

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