Sustaining rural maternity care--don't forget the RNs.
Bibliographic record
Abstract
INTRODUCTION: Registered nurses provide intrapartum care to women who choose to have their babies in hospital. Considering the current national shortage of nurses, the ability of registered nurses to continue to care for women, especially in small rural hospitals, is a critical concern. PURPOSES: The purposes of the study were 1) to conduct a systematic review of the maternal-child-nursing literature in rural locations; and 2) to identify one rural Ontario hospital where nurses and physicians deliver care to women with low-risk pregnancies, and then conduct an institutional ethnography to understand the enablers and barriers to low-risk rural maternity care. METHODS: A literature search was conducted to determine the state of rural registered nurses; and a telephone survey of 25 rural Ontario hospitals was undertaken to locate a hospital in which an institutional ethnography study could be conducted. RESULTS: Registered nurses in rural areas are more likely to be multi-specialists than generalists because of the need to adapt to emergencies across the life continuum. To care for pregnant women and their families, registered nurses require many of the same considerations that physicians have outlined: access to continuing education, appropriate call-back schedules, support from other health care professionals and administrators, and a value system that respects their expertise. Results from the ethnography of one Ontario health care institution revealed that when these aforementioned considerations are addressed, registered nurses are able to provide safe, comprehensive low-risk care in a rural maternity programme. CONCLUSIONS: Registered nurses play an important collaborative role in maternity care. We need Canadian data on registered nurses so that we can educate, recruit and retain them to care for women with low-risk pregnancies in rural and remote ares of Canada. Nursing services should be reviewed. Collaborative care models integrating newer professionals such as midwives, as well as understanding the role of doulas, may help in developing sustainable care to rural women.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".