Remotely Versus Locally Facilitated Simulation-based Training in Management of the Deteriorating Patient by Newly Graduated Health Professionals
Bibliographic record
Abstract
INTRODUCTION: This study evaluated delivery of immersive simulation-based training (SBT) by distance education. Newly graduated health professionals' experience of and learning outcomes from videoconference-enabled remotely facilitated (RF) were prospectively compared with a locally facilitated (LF) format within a course addressing management of the deteriorating patient. METHODS: Participants were exposed to both RF and LF formats in an intervention course (IC). The primary outcome measure was a questionnaire detailing participants' experience of 1 RF scenario and 1 LF scenario. The 16-item questionnaire measured perceived learning, comfort, interaction with other learners and instructor, as well as quality of instruction, factors that are considered essential in both SBT and distance education. As a secondary outcome measure, learning outcomes, measured as precourse and postcourse scores and pass rates in multiple-choice question tests, were also measured and compared with those of participants completing control courses, in which only the LF format was used. RESULTS: The study was conducted between April 2013 and April 2014. Among the 155 participants who participated in ICs, questionnaire results revealed a small, significantly higher median total score (25-75 interquartile range) for LF versus RF format scenarios [78 (72-80) vs. 76 (68-80), P = 0.01]. Multiple-choice question test scores compared between 155 IC and 150 control course participants showed no significant differences. CONCLUSIONS: Participants' experience of SBT using the RF format was slightly less positive than the LF format; however, it had no measured impact on knowledge. The impact of RF-SBT on more complex training applications remains poorly understood. Instructors could potentially optimize learner comfort and engagement by improving their interactive skills.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".