Transfer of learning and patient outcome in simulated crisis resource management: a systematic review
Bibliographic record
Abstract
PURPOSE: Simulation-based learning is increasingly used by healthcare professionals as a safe method to learn and practice non-technical skills, such as communication and leadership, required for effective crisis resource management (CRM). This systematic review was conducted to gain a better understanding of the impact of simulation-based CRM teaching on transfer of learning to the workplace and subsequent changes in patient outcomes. SOURCE: Studies on CRM, crisis management, crew resource management, teamwork, and simulation published up to September 2012 were searched in MEDLINE(®), EMBASE™, CINAHL, Cochrane Central Register of Controlled Trials, and ERIC. All studies that used simulation-based CRM teaching with outcomes measured at Kirkpatrick Level 3 (transfer of learning to the workplace) or 4 (patient outcome) were included. Studies measuring only learners' reactions or simple learning (Kirkpatrick Level 1 or 2, respectively) were excluded. Two authors independently reviewed all identified titles and abstracts for eligibility. PRINCIPAL FINDINGS: Nine articles were identified as meeting the inclusion criteria. Four studies measured transfer of simulation-based CRM learning into the clinical setting (Kirkpatrick Level 3). In three of these studies, simulation-enhanced CRM training was found significantly more effective than no intervention or didactic teaching. Five studies measured patient outcomes (Kirkpatrick Level 4). Only one of these studies found that simulation-based CRM training made a clearly significant impact on patient mortality. CONCLUSIONS: Based on a small number of studies, this systematic review found that CRM skills learned at the simulation centre are transferred to clinical settings, and the acquired CRM skills may translate to improved patient outcomes, including a decrease in mortality.
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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 | 0.014 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".