Making Nursing Mistakes Without Patient Risk: Simulation to the Rescue!
Bibliographic record
Abstract
Perils to patient safety, the bulk of the responsible errors involving medications, account for unfathomable costs within our healthcare system (IOM, 1999; CPSI, 2003). Patient safety has been and continues to be in jeopardy and is of paramount importance in the Canadian healthcare system. Patient safety initiatives have included examination of current educational strategies and new initiatives to aid in reducing these costly errors as a result of adverse events that were preventable (CPSI, 2003). High-fidelity patient simulation can provide promising patient safety solutions to assist in the teaching and learning environments within nursing educational programs. Faculty teaching and learning initiatives, utilizing the high-fidelity human patient computer-controlled simulation (HHPCS) as an adjunct technology in the Bodnar Simulation Suites of the new Robbins Health Learning Centre in Edmonton, Alberta, have included examining the capabilities to thwart the reality of making mistakes in patient safety, including medication administration errors, without compromising the safety, even potentially the lives, of real patients. Development of new patient safety modules utilizing the HHPCS provides further evidence of its importance as an adjunct technology within the context of nursing educational programs. This paper is presented to promote further discourse on the potential of highfidelity simulation technology as a vital tool in the future of nursing education. Designing and establishing effective educational and professional development programs in simulation are proposed to be integral to building a safer healthcare system. Further research is recommended to authenticate the role of high -fidelity human simulation technologies in the pursuit of better learning outcomes and inevitably improved patient safety outcomes.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".