Status of simulation in health care education: an international survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Simulation is rapidly penetrating the terrain of health care education and has gained growing acceptance as an educational method and patient safety tool. Despite this, the state of simulation in health care education has not yet been evaluated on a global scale. In this project, we studied the global status of simulation in health care education by determining the degree of financial support, infrastructure, manpower, information technology capabilities, engagement of groups of learners, and research and scholarly activities, as well as the barriers, strengths, opportunities for growth, and other aspects of simulation in health care education. We utilized a two-stage process, including an online survey and a site visit that included interviews and debriefings. Forty-two simulation centers worldwide participated in this study, the results of which show that despite enormous interest and enthusiasm in the health care community, use of simulation in health care education is limited to specific areas and is not a budgeted item in many institutions. Absence of a sustainable business model, as well as sufficient financial support in terms of budget, infrastructure, manpower, research, and scholarly activities, slows down the movement of simulation. Specific recommendations are made based on current findings to support simulation in the next developmental stages.
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.
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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it