“My skills are going to be exposed” – Anxiety, meaning and professional identity during simulation-based learning in medical students: A mixed method study
Bibliographic record
Abstract
Clinical simulation is an established part of the educational offering for healthcare workers, yet for many it can induce feelings of anxiety and uncertainty. The role of these feelings in enhancing or impeding the pedagogical experience is unclear, but it is likely that there are mediating and moderating factors that affect the relationship. We undertook a prospective, observational, mixed methods study of a cohort of medical students undergoing a paediatric critical illness simulation course. Our quantitative aims were to understand whether increased state anxiety correlated negatively with simulation effectiveness. Our qualitative aim was to understand how students experienced their anxiety in relation to their learning experience during the simulation. We found that there was no significant relationship between state anxiety and simulation effectiveness. However, during qualitative interviews, we uncovered a rich seam of material regarding the students' experience which coalesced around three broad themes - anticipation of the unknown, the nature of being observed, and the realism of clinical simulation, which elicited reflections among students on their future professional responsibilities and identity. We found that these feelings were simultaneously triggers for anxiety - and we suggest some practical ways of reducing the stress associated with this - and also the gateway to great insight for students into their learning process. Traditionally, learning in medicine has been associated with a maladaptive culture of perfection - high academic expectations, the perception that the ability to recall under pressure is the hallmark of a good doctor, and unwillingness to admit to vulnerability or error. Simulation leads students to the understanding that successful performance in real clinical settings requires asking for help, recognising the demands on and the limitations of available resources, and being able to navigate uncertainty.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".