Managing student workload in clinical simulation: a mindfulness-based intervention
Bibliographic record
Abstract
Background: Simulation places multiple simultaneous demands on participants. It is well documented in the literature that many participants feel performance stress, anxiety or other emotions while participating in simulation activities. These feelings and other stressors or distractions may impact participant ability to engage in simulation. The use of mindfulness has been proven to enhance performance in other contexts and we wondered if including a mindful moments activity in the traditional prebrief would change the participants perceived workload demands. Method: Using a fourth-year undergraduate nursing course with an intense simulation requirement we were able to compare a control group to an intervention group who was exposed to this mindful moment activity. All participants completed the same simulations. Postsimulation event, all participants completed the National Aeronautics and Space Administration Task Learning Index which measures mental demands, physical demands, temporal demands, effort, performance and frustration. Our convenience sample consisted of 107 nursing students (86 treatment group, 21 control group) who participated in 411 simulations for this study. Results: The control group experienced significantly different perceived workload demands in two domains (temporal and effort). Conclusion: It is possible to manipulate participants' perceived workload in simulation learning experiences. More research is needed to determine optimal participant demand levels. We continue in our practices to use this technique and are currently expanding it to use in other high stress situations such as before examinations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".