Selected abstracts from the 2019 Simulation Summit
Bibliographic record
Abstract
Background: Instruction that encourages trainees to integrate conceptual "why" and procedural "how" knowledge improves their transfer of procedural skills.For training away from the bedside and direct supervision, questions remain on how to represent the causal relationship between clinical concepts and procedural actions (e.g., how patient anatomy relates to inserting a needle).Simulation presents a unique education modality for delivering causal instruction that can help trainees build cognitive connections between the theoretical concepts and procedural actions of clinical skills.We varied the modality and level of interactivity when presenting these causal relationships during simulation-based lumbar puncture (LP) training and measured impacts on participants' retention and transfer.Research Question: When delivering integrated instruction, how does the modality (video-based or simulation-based) impact trainees' skill retention and transfer?Methods: During a 1-hour session, we randomized 66 medical students to one of three instructional interventions: i) procedural-only video-based instruction, ii) integrated video-based instruction, and iii) integrated simulation-based instruction.One-week later, we tested participants' LP skill retention and transfer, and their conceptual knowledge on a written test.Results: Simple mediation regression analyses revealed that participants receiving integrated instruction had superior LP retention and transfer skills via gains in conceptual knowledge (all p<0.01).We found no significant performance differences between the integrated groups (p>0.01).Participants receiving procedural-only instruction practiced significantly more LPs during training (M=2.36)than participants receiving integrated video-based (M=1.82) and simulationbased instruction (M=1.50),p<0.05.Conclusion: Trainees' ability to create cognitive connections between conceptual and procedural knowledge appears to improve when they interact with instructional materials highlighting the causal relationships between these knowledge types.Simulation experiences can be designed to make abstract clinical concepts visible using hands-on, interactive modules, which enhances trainees' conceptual knowledge, as well as, their skill retention and transfer.However, integrated instruction reduced participants' time to practice LP scenarios, which may have reduced the effectiveness of our efforts to promote such "cognitive integration".We suggest that more advanced trainees with baseline procedural proficiency may experience greater benefits from such integrated instruction.Integrated instruction may improve trainees' skill retention and transfer, despite reducing how many LPs they could practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.186 | 0.072 |
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".