A pilot initiative to enhance quality improvement teaching with simulation
Bibliographic record
Abstract
BACKGROUND: Quality Improvement and Patient Safety (QIPS) is a recognised competency across residency programmes. Although a variety of teaching modalities exist, many do not represent the multifaceted clinical environment that trainees work in. Residents have reported challenges in linking QIPS classroom-based learning with their clinical duties. High-fidelity simulation has been used to bridge this gap within clinical skills teaching and therefore has potential to address this issue in QIPS learning. APPROACH: We developed and piloted four high-fidelity simulation scenarios with 15 surgical residents (Orthopaedics, General Surgery, Gynaecology and Neurosurgery). Each scenario contained elements of both latent and active safety errors. Residents were provided with a short pre-reading from an open-access resource on basic QIPS methodology and underwent a debriefing by a trained QIPS faculty. Residents were then tasked to apply their learning to their scenario to develop a QIPS-focused solution. EVALUATION: Objective knowledge acquisition was assessed with the Quality Improvement Knowledge Assessment Tool-Revised (QIKAT-R) in conjunction with a survey based upon the Kirkpatrick Model of Learning. Overall, residents agreed that the simulation was helpful in learning QIPS methodology and agreed that they could perform fundamental QIPS tasks. The average QIKAT-R score demonstrated a trend towards improvement. IMPLICATIONS: High-fidelity simulation is a potential means to provide residents with hands-on experience in QIPS knowledge acquisition and application. Future directions should aim to compare the efficacy of simulation with other teaching modalities and evaluate the long-term impact of QIPS teaching on resident behaviours and motivation to take part in QIPS initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".