Canadian Rural Women’s Experiences with Rural Primary Health Care Nurse Practitioners
Bibliographic record
Abstract
Background: In Canada, one in five women lives in a rural area. These rural women often experience different health challenges than urban women, including lower life expectancy, higher rates of disability and cancer, fewer available health care resources and greater distances to access health care services. Nurse practitioners [NPs] provide important primary health care [PHC] services to rural women.Research Objective: The purpose of this research study was to explore rural women’s experiences with primary health care nurse practitioners [PHCNPs].Method and Sample: In-depth, face-to-face interviews using interpretive description methodology were conducted with nine rural women, aged 18-80, who used NP services in rural southwest Ontario, Canada.Results: The participants in the study particularly appreciated the nursing knowledge of the NP, the time the NPs spent with them, and the thoroughness of the care provided by NPs. These foundational elements of the participants’ experiences with rural NPs created a sense of trust and respect, which lead to a collaborative partnership between the NP and the rural women.Conclusions: Results of this study suggest that these rural women were overwhelmingly satisfied with the care provided by NPs. In particular, they valued the collaborative partnership with the NP. These findings have important implications for rural health care practice, policy, and education.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".