Planning and Providing End‐of‐life Care in Rural Areas
Bibliographic record
Abstract
CONTEXT: Approximately 20% of North Americans and 25% of Europeans reside in rural areas. Planning and providing end-of-life (EOL) care in rural areas presents some unique challenges. PURPOSE: In order to understand these challenges, and other important issues or circumstances, a literature search was conducted to assess the state of science on rural EOL care. METHODS: The following databases were searched for articles published from 1988 through 2003: EMBASE, Medline, CINAHL, AHMED, Psychinfo, ERIC, HealthStar, Sociological Abstracts, and Cochrane. All articles were systematically reviewed. FINDINGS: Thirty-six research articles were identified. Only 1 randomized controlled trial was located. Most research was single site, small sample, and exploratory/descriptive in design. Four distinct foci in this body of research were noted: (1) identifying and describing differences between urban and rural EOL care; (2) exploring rural EOL care; (3) assessing the EOL needs and wishes of terminally ill or dying persons, their family members, and health care professionals in rural areas; and (4) exploring EOL education for rural EOL care providers. CONCLUSIONS: Although rural EOL care research is not extensive, the existing literature is helpful for realizing the importance of EOL care in rural communities, as well as for conceptualizing and planning EOL care in rural communities. One of the chief considerations for rural EOL care is that dying at home is a common wish, with home-based nursing care a key factor for this to become a reality. Another chief consideration is ensuring all rural health care professionals are both prepared for and supported while delivering EOL 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".