Operating Room Assistant Program
Bibliographic record
Abstract
In this presentation, Kavitha Nadarajah-Gbeve discusses the innovative Operating Room Assistant (ORA) program initiated in Manitoba to address the critical shortage of qualified nursing staff in operating rooms. Kavitha, a nursing professional with a vast background in adult surgical care, outlines her educational credentials and professional roles while emphasizing her contributions as the surgery program educator at Grace Hospital, Winnipeg. Her talk focuses on the challenges posed by nursing shortages, especially exacerbated by the COVID-19 pandemic, leading to significant surgical backlogs. The ORA program was designed to provide essential support in the operating room by training individuals who have completed a comprehensive healthcare aide course and meet specific criteria, including experience in acute care settings. The program consists of a 10-week online training course paired with in-person lab sessions and a clinical practicum, intended to equip new assistants with vital skills and knowledge in surgical procedures, including anatomy, sterilization techniques, and the handling of surgical instruments. Kavitha emphasizes the importance of adhering to the standards set by the Orthopedic Nurses Association of Canada (ORNAC) and outlines the responsibilities of ORAs, which involve direct support to surgical teams, patient positioning, and maintaining a sterile environment. Kavitha also shares the program's current success and future goals, with an expectation of training around 70 ORAs by the end of the year to continue improving surgical service delivery. Overall, her presentation highlights a proactive approach to tackling nursing shortages and enhancing patient care within surgical environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.241 | 0.063 |
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".