Innovations in health service organization and delivery in northern rural and remote regions: a review of the literature
Bibliographic record
Abstract
OBJECTIVES: To identify and review innovations relevant to improving access, quality, efficiency and/or effectiveness in the organization and delivery of health care services in rural and remote areas. STUDY DESIGN: Literature review. METHODS: Key bibliographic databases that index health research were searched: MEDLINE, EMBASE and CINAHL. Other databases relevant to Arctic health were also accessed. Abstracts were assessed for relevancy and full articles were reviewed and categorized according to emergent themes. RESULTS: Many innovations in delivering services to rural and remote areas were identified, particularly in the public health realm. These innovations were grouped into 4 key themes: organizational structure of health services; utilization of telehealth and ehealth; medical transportation; and public health challenges. CONCLUSIONS: Despite the challenges facing rural and remote regions, there is a distinctly positive message from this broad literature review. Evidence-based initiatives exist across a range of areas - which include operational efficiency and integration, access to care, organizational structure, public health, continuing education and workforce composition - that have the potential to positively impact health care quality and health-related outcomes.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".