Making a difference: using the safe surgery checklist to initiate continuing education for perioperative nurses in low-income settings.
Bibliographic record
Abstract
The WHO Safe Surgery Checklist (2008) patient safety focus and communication prompts are widely accepted. In many low-income regions (as defined by the World Bank and accepted by the World Health Organization) perioperative nurses have little or no formal training; continuing and in-service education are virtually unknown; nor does an articulated "culture of safety" exist. In 2009 the Canadian Network for International Surgery (CNIS) piloted a two-day perioperative nursing course, in Addis Ababa, Ethiopia, using lectures, case studies, skills sessions, and role-play exercises based on the SSSL Checklist outline and protocols. Canadian instructors (who are certified after taking the Canadian Network for International Surgery-sponsored Instructor's Course) have since returned and taught at additional sites in Ethiopia and Uganda. Course participants now include perioperative nurses, anaesthetists, and junior surgical residents--mirroring the interdisciplinary teamwork that is crucial to safe perioperative patient care. The course's facilitated discussions focus on workplace and practice issues in order to allow for appropriate evaluation and planning of future educational initiatives. Participants complete pre- and post-course questionnaires, which evaluate baseline and post-course knowledge, and further follow-up is completed four months after course completion. This article explains the need for aiding in the expansion of perioperative nursing knowledge and skill in low-income settings and provides the author's personal perspective and experience in responding to this need. Her experience as facilitator in a pilot project and subsequent course development described. The objective is to discuss ways that other perioperative nurses can work to make a positive difference on professional practice and patient care in low-income regions.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".