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Record W2171863969

Rural women and pharmacologic therapy: needs and issues in rural Canada.

2008· article· en· W2171863969 on OpenAlexaffabout
Beverly Leipert, Doreen Matsui, Jessica Wagner, Michael Rieder

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsRural areaMedicineMedical prescriptionConfidentialityQualitative researchHealth careRural healthFamily medicineNursingEconomic growthSociology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The needs and issues of rural women regarding pharmacologic information and therapy are rarely explored. We sought to explore the needs and issues of rural women in Canada regarding drug-related information and prescription and nonprescription pharmaceuticals. METHODS: We used the qualitative methodology of interpretive description. In-depth semistructured face-to-face interviews were conducted with 20 women aged 17-88 years who lived in rural southwestern Ontario. RESULTS: Although rural women accessed prescription medications, complementary and alternative medicine (CAM) was highly favoured, and alcohol and illicit drugs such as marijuana, crystal meth and cocaine were prevalent in rural communities. Factors that affected rural women's decisions about which medications to use included access to health care practitioners, costs of medications, experiences of family members and friends with prescribed and alternative medications, attitudes and approaches of health care providers and health store employees, and the women's own expectations and desires. Factors that affected the use of illicit drugs included availability, boredom, peer pressure and cultural norms. Rural factors that influenced access to drug information and use included presence or lack of confidential care, distance to resources, and presence, accessibility and acceptability of rural resources. CONCLUSION: Rural women use a variety of drug therapies and sources of information, and experience unique socioeconomic and environmental issues that affect access to appropriate drug-related information and therapies. Further research is needed to clarify and articulate pharmacologic needs, issues and solutions for women in diverse rural settings.

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 score0.133
Threshold uncertainty score0.860

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 head0.044
GPT teacher head0.353
Teacher spread0.309 · 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

Citations16
Published2008
Admission routes2
Has abstractyes

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