Simulation-Based Clinical Education in The Operating Room: A Review Study
Bibliographic record
Abstract
Context: Simulation is an educational technology that has been demonstrated to facilitate learning and enhance learners' performance. The primary objective of this study is to introduce and investigate the use of simulation-based education in the context of clinical education within the operating room. Evidence Acquisition: For this review article, the keywords "Simulation," "Education," "Clinical Education," "Operating Room Education," and "Simulation in the Operating Room" were utilized to conduct a comprehensive search of articles available on PubMed, Google Scholar, Scopus, Web of Science, and Science Direct from the period of 2000 to 2022. Articles about the introduction and implementation of simulation-based education methods in the context of the operating room were selected and examined. Results: A total of 42 articles were scrutinized, which encompassed discussions on the historical evolution and significant role of simulation in clinical education, the approaches involved in constructing and advancing simulation-based education, the diversity of simulators employed in the operating room, and the significance and variations of models created to evaluate the efficacy of such methods. The simulators described included physical simulators with low fidelity, web-based educational tools, computer-based video training, virtual learning systems, learning management systems, the "McGill system" for laparoscopic skills training and evaluation, simulation-based surgical methods, and computer-controlled mannequins such as "Sim Man 3G". Conclusions: The implementation of various simulators and models in the context of operating room education presents opportunities for the design, implementation, and evaluation of educational programs. With proper planning and attention to detail, many of the existing challenges can be effectively addressed.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".