Rural healthcare delivery: Navigating a complex ecosystem
Bibliographic record
Abstract
Clinical Practice Guidelines (CPGs) provide evidence-based recommendations for Healthcare Providers (HCPs) to utilize when making patient care decisions. Rural providers face challenges in the provision of evidence-based care, including the use of guidelines. The aim of this article is to explore the complexities of providing healthcare in rural areas. This article will focus on a specific aspect of rural maternity care with well-established CPGs, the prevention of Rhesus D factor alloimmunization. An applied health research approach, interpretive description, utilized semistructured interviews with HCPs across the vast geographic region of northern British Columbia. The study found that HCPs are aware of guidelines but face various barriers during implementation. In order to implement guidelines within practice, rural HCPs adapt processes to overcome local barriers. These process adaptations need to be identified and shared across a large health authority with a complex geography and healthcare system to ensure quality of 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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".