Rural Prenatal Care by Nurse Practitioners: A Narrative Review
Bibliographic record
Abstract
Background: Rural Canadian populations face many challenges due to their geographical isolation, including inaccessible and inequitable primary health care. Specifically, pregnant women are at risk of not receiving prenatal care (PNC) due to physical and social barriers. Inadequate PNC can have detrimental effects on both maternal and neonatal health outcomes. Nurse practitioners (NPs) are an essential group of alternative primary care providers who can provide specialized care, including PNC, to these underserved populations. Objective: The purpose of this narrative review was to identify existing NP-led rural PNC programs in other health care systems to support maternal and neonatal outcomes. Methods: A systematic search was performed to identify articles published between 2002 and 2022 on CINAHL (EBSCO host) and MEDLINE (OVID). Literature was excluded if (1) the context was based in urban centers; (2) the study focused on specialized obstetrical/gynecological-based care; or (3) the study was published in a language other than English. The literature was assessed and synthesized into a narrative review. Results: The initial search identified 34 potentially relevant articles. Five broad themes were identified, including (1) barriers to care; (2) mobile health clinics; (3) collaborative or tiered models of care; (4) telemedicine; and (5) NPs as essential primary care providers. Conclusions: The introduction of a collaborative NP-led approach to rural Canadian settings has the potential to address barriers to PNC and provide efficient, equitable, and inclusive health care.
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 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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".