Nursing Education: A Catalyst for the Patient Safety Movement
Bibliographic record
Abstract
Creating a culture of safety in healthcare systems is a goal of leaders in the patient safety movement. Commitment of leadership to safety in the Saskatchewan Institute of Applied Science and Technology (SIAST) Nursing Division has resulted in the development of the Patient Safety Project Team (PSPT) and a steady shift in the culture of the organization toward a systems approach to patient safety. Graduates prepared with the competencies necessary to be diligent about their practice and skilled in determining the root causes of system error in healthcare will become leaders in shifting the healthcare culture to strengthen patient safety. The PSPT believes this cultural shift begins with the education system. It involves modifications to curricula content, facilitation of multidisciplinary processes, and inclusion of theory and practice that reflect critical inquiry into healthcare and nursing education systems to ensure patient safety. In this paper the practical approaches and initiatives of the PSPT are reviewed. The integration of Patient Safety Core Curriculum modules for competency development is described. The policy for reporting adverse events and near misses is outlined. In addition, the student-focused reporting tool, the results and the implications for teaching in the clinical setting are discussed. Processes used to engage faculty are also addressed.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 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".