Improving Access to Safe Anesthetic Care in Rural and Remote Communities in Affluent Countries
Bibliographic record
Abstract
Inadequate access to anesthesia and surgical services is often considered to be a problem of low- and middle-income countries. However, affluent nations, including Canada, Australia, and the United States, also face shortages of anesthesia and surgical care in rural and remote communities. Inadequate services often disproportionately affect indigenous populations. A lack of anesthesia care providers has been identified as a major contributing factor to the shortfall of surgical and obstetrical care in rural and remote areas of these countries. This report summarizes the challenges facing the provision of anesthesia services in rural and remote regions. The current landscape of anesthesia providers and their training is described. We also explore innovative strategies and emerging technologies that could better support physician-led anesthesia care teams working in rural and remote areas. Ultimately, we believe that it is the responsibility of specialist anesthesiologists and academic health sciences centers to facilitate access to high-quality care through partnership with other stakeholders. Professional medical organizations also play an important role in ensuring the quality of care and continuing professional development. Enhanced collaboration between academic anesthesiologists and other stakeholders is required to meet the challenge issued by the World Health Organization to ensure access to essential anesthesia and surgical services for all.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".