Rural women and pharmacologic therapy: needs and issues in rural Canada.
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".