Applying Flipped Learning Concepts to Simulation and Its Impact on the Retention of Non-technical Skills
Bibliographic record
Abstract
Background To Err is human, building a safer health care system has put the emphasis on the importance of teamwork practice through simulation based medical education (SBME) to prevent patient mortality. Flipped learning has emerged as an effective add-on to simulation for the acquisition of technical skills (TS). However, data on the combination of flipped learning and SBME for the acquisition of non-technical skills, such as crisis resource management (CRM), is lacking. Purpose The primary objective of our study was to assess if providing flipped learning regarding the TS required for the taskwork of an upcoming simulation would increase CRM skills retention three months after the study intervention. Secondary objectives concerned the interventions’ effect on anxiety levels, cognitive load, achievement emotions and participants’ perceptions towards the activity. Methods A randomized between-group experiment using a convergent mixed method approach was employed. The study intervention consisted in the provision of beforehand teaching on the different illnesses encountered in an upcoming acute care simulation training session. The primary outcome was the between group difference in overall scores on the Ottawa CRM Global Rating Scale. Main and secondary quantitative outcomes were assessed using a repeated measures ANOVA. A qualitative analysis was conducted on the verbatim of semi-structured interviews. Secondary qualitative outcomes were assessed both deductively through a content analysis and inductively with a thematic analysis. Results Sixty-four participants underwent randomization. There was a between subject difference favouring flipped learning. No differences in salivary cortisol, heart rate, state trait anxiety inventory or achievement emotions were noted between groups. The qualitative analysis revealed a high variability in the intervention’s impact on participants, with some experiencing performance pressure. A strong intuition towards perceiving judgement was associated with simulation training. Learners’ achievement goal orientation may have modulated their reaction to the study intervention. Discussion Our findings are in line with previous work on the topic of flipped learning and SBME. However, our trial provides insight on potential negative effects it may have on learners. Educators should utilize this instructional design with caution until further research may clarify which learner subtype may benefit or not from this combination.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".