Electronic Feedback Alone Versus Electronic Feedback Plus in-Person Debriefing for a Serious Game Designed to Teach Novice Anesthesiology Residents to Perform General Anesthesia for Cesarean Delivery: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: EmergenCSimTM, is a novel researcher-developed serious game (SG) with an embedded scoring and feedback tool that reproduces an obstetric operating room environment. The learner must perform general anesthesia (GA) for emergent cesarean delivery (CD) for umbilical cord prolapse. The game was developed as an alternative teaching tool because of diminishing real-world exposure of anesthesiology trainees to this clinical scenario. Traditional debriefing (facilitator-guided reflection) is considered to be integral to experiential learning but requires the participation of an instructor. The optimal debriefing methods for SGs have not been well-studied. Electronic feedback is commonly provided at the conclusion of SGs, so we aimed to compare the effectiveness of learning when an in-person debrief is added to electronic feedback compared to using electronic feedback alone. OBJECTIVE: We hypothesized that an in-person debriefing in addition to the SG-embedded electronic feedback will provide superior learning than electronic feedback alone. METHODS: Novice 1st year anesthesiology residents (CA-1) (n=51) (i) watched a recorded lecture on GA for emergent CD, (ii) took a 26-item multiple-choice question (MCQ) pre-test, and (iii) played EmergenCSimTM (maximum score 196.5). They were randomized to either the control group which experienced the electronic feedback alone (Group EF, n=26) or the intervention group, which experienced the SG-embedded electronic feedback and an in-person debriefing (Group IPD+EF, n=25). All subjects played the SG a 2nd time, with instructions to try to increase their score, then they took a 26-item MCQ post-test. Pre-and post-tests (maximum score of 26 points each), were validated parallel forms. RESULTS: For Groups EF and IPD+EF respectively, mean pre-test scores were18.6 (SD 2.5) and 19.4 (SD 2.3), and mean post-test scores were 22.6 (SD 2.2) and 22.1 (SD 1.6); F=1.8, P =.19. SG scores for Groups EF and IPD+EF respectively were - mean 1st play SG scores of 135 (SE 4.4) and 141 (SE 4.5), and mean 2nd play SG scores were 163.1 (SE 2.9) and 173.3 (SE 2.9); F= 137.7, P < .001. CONCLUSIONS: Adding an in-person debriefing experience led to greater improvement in SG scores, emphasizing the learning benefits of this practice. Improved SG performance in both groups suggests that SGs have a role as independent, less resource-intensive educational tools. CLINICALTRIAL: None.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".