Healthcare Simulation: An effective way of learning in health care
Bibliographic record
Abstract
Background and Objective: Simulation-based learning has been a part of teaching in healthcare for a long time; however, in recent decades, simulation-based learning has been adopted by a significant number of healthcare institutes at different levels to improve practical skills, confidence, and preparedness to ensure patient safety and its application in real-life situations towards better patient care. The main objective of this paper was to use existing literature to explore aspects of simulation in healthcare teaching. Methods: It is a narrative review on simulation in healthcare that was conducted by using various search engines for English-language articles published between 2010 and August 2020. The main search terms were simulation, healthcare teaching, and simulation in healthcare. All articles found relevant to the title and/or abstract were retrieved. Searches were conducted using the academic databases PubMed, Google Scholar, MEDLINE, CINAHL, and Athabasca University (AU) library site. The studies were reviewed if they were considered relevant to the search by the primary authors. Results: Thirty-nine articles, which met the pre-set criteria, were analyzed and employed as a reference in this paper to support the idea that simulation is an effective way of learning in healthcare. Conclusion: This paper reviewed various aspects of simulation, including its background, philosophies, and highlighted the advantages and disadvantages of incorporating simulation as a pedagogical approach into current educational curriculums for healthcare students. Furthermore, it presents a brief discussion on the current uses of simulation, followed by the educational strategies related to simulation and the importance of debriefing in simulation activities. doi: https://doi.org/10.12669/pjms.39.4.7145 How to cite this: Saleem M, Khan Z. Healthcare Simulation: An effective way of learning in health care. Pak J Med Sci. 2023;39(4):1185-1190. doi: https://doi.org/10.12669/pjms.39.4.7145 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".