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Record W6980702598

COMPLEMENTARY AND ALTERNATIVE MEDICINE USE BY CANADIAN RURAL WOMEN: A SECONDARY ANALYSIS

2010· article· en· W6980702598 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2010
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAlternative medicineQualitative researchMedical prescriptionHealth careAffect (linguistics)Qualitative analysisRural healthQualitative property
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this secondary qualitative study is to examine rural women’s experiences with Complementary and Alternative Medicine (CAM) use and factors that affect use. In the original study, twenty rural women from Southwest Ontario were interviewed to explore rural women’s use of prescription and non-prescription pharmaceuticals. In the research described here, qualitative secondary analysis methodology was used to answer new research questions distinct from the original study. Analysis of the original data has revealed the following findings: facilitators of CAM use, barriers to CAM use, facilitators and barriers to CAM use, comparing conventional medication use and CAM use, and evaluating effectiveness of CAM. Implications for nursing practice, education, and future research are noted. In conclusion, this study suggests that, because participants used CAM as an important health care choice, nurses and health care professionals need to consider CAM into their practice and education.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0100.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.083
GPT teacher head0.338
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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