ASA Global Health Overseas Training Programs: A Rwanda Update
Bibliographic record
Abstract
Features| April 2023 ASA Global Health Overseas Training Programs: A Rwanda Update Shyamal R. Asher, MD, MBA; Shyamal R. Asher, MD, MBA Search for other works by this author on: This Site PubMed Google Scholar Ana Maria Crawford, MD, MSc, FASA Ana Maria Crawford, MD, MSc, FASA Search for other works by this author on: This Site PubMed Google Scholar ASA Monitor April 2023, Vol. 87, 22. https://doi.org/10.1097/01.ASM.0000924980.08965.a9 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Search Site Citation Shyamal R. Asher, Ana Maria Crawford; ASA Global Health Overseas Training Programs: A Rwanda Update. ASA Monitor 2023; 87:22 doi: https://doi.org/10.1097/01.ASM.0000924980.08965.a9 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsASA Monitor Search Advanced Search Topics: rwanda, world health ASA's Committee on Global Health, in partnership with the Canadian Anesthesiologists' Society International Education Foundation (CASIEF), remains actively engaged in the University of Rwanda anesthesiology residency program. Established in 2006, this partnership continues to work on building capacity for safe anesthesia practices across Rwanda. Over the years, ASA volunteers have traveled to Rwanda and worked in close collaboration with local faculty to support resident didactics, intraoperative teaching, ICU rounds, and simulation workshops. ASA volunteers have learned much from Rwandan colleagues regarding innovation during shortages, cost containment, and providing clinical care with less environmental impact. Although the COVID pandemic forced a pause in travel to Rwanda in 2020 and 2021, there is renewed strength in the program with those returning to Rwanda and a number of first-time volunteers. Over 30 anesthesiologists have successfully graduated from the anesthesiology residency program, many remaining in Rwanda to expand safe anesthesia access. Some serve as... You do not currently have access to this content.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.023 |
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".