Rural health care and the nurse anesthetist.
Bibliographic record
Abstract
As they work in all types of practice arrangements and settings with and without anesthesiologists, certified registered nurse anesthetists have been and continue to be the principle anesthesia providers in rural hospitals in the United States. As such, they are responsible for providing anesthesia services to about 1 quarter of the US population that resides in rural and frontier areas of this country. Rural health care is characterized by its necessity to provide a broad array of services with lesser resources than are typically available in metropolitan or urban areas. The rural population is characterized as having a higher proportion of elderly people and children under the age of 18, a higher incidence of chronic diseases, a lower mortality rate (albeit a slightly higher infant mortality rate), and a 40% higher mortality rate resulting from accidents. Rural residents are poorer and less likely to have job-related health insurance benefits or Medicare supplemental insurance. Despite the significant rise in the number of anesthesiologists in the past 10 to 15 years, there is no evidence that they are attracted to practice in these areas. As sole anesthesia providers in many of these rural hospitals, rural CRNAs have both common and unique problems and issues that confront them. However, from available reports, their communities are satisfied with their services, providing evidence of the capability of CRNAs to function satisfactorily without the anesthesiologist.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".