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Record W2176588573

Investigating Complementary and Alternative Medicine Use Among Seniors

2015· article· en· W2176588573 on OpenAlexaffvenue
Karl Ward, Renée S. MacPhee

Bibliographic record

VenueUBC Faculty of Medicine medical journal · 2015
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMedicineFocus groupAlternative medicineQualitative researchFamily medicinePsychologyPathology
DOInot available

Abstract

fetched live from OpenAlex

Abstract Objectives: Complementary and alternative medicine (CAM) is used regularly by 70% of Canadians, 2,3 but when compared to younger users ofCAM, seniors tend to use it less frequently. Using a phenomenological approach, this study sought to explore the attitudes and beliefs of seniors towards the use ofCAM.  Methods: This qualitative study used either in-depth personal interviews or focus group interviews as the primary means of data collection. Participants in the study were individuals who had either usedCAM in the past, or who were currently usingCAM. Results: Participants described that they would use conventional treatment for pathological disease, but would prefer to useCAM in certain circumstances as it was perceived to be a more natural approach. Exercise was also described as a form ofCAM. Deterrents forCAM use include: limited scientific evidence; cost; and the attitudes of others (e.g., physicians, the public).  Conclusion: Participants felt that they had positive experiences usingCAM as an adjunct to conventional medicine, and felt that they had no personal barriers to accessingCAM. A major deterrent ofCAM use was the limited scientific evidence, while minor factors included cost and the attitudes of others. Open discussion aboutCAM use should take place between physician and patients.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.157
GPT teacher head0.396
Teacher spread0.239 · 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.

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

Citations0
Published2015
Admission routes2
Has abstractyes

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