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Record W4412124776 · doi:10.1371/journal.pone.0327306

“My skills are going to be exposed” – Anxiety, meaning and professional identity during simulation-based learning in medical students: A mixed method study

2025· article· en· W4412124776 on OpenAlexaff
Gareth Drake, Niki Skaltsa, Kritika Kalia, Chibueze Ogbonnaya, Asha Padhair, Samuel Morrish, Pratheeban Nambyiah

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMeaning (existential)AnxietyIdentity (music)PsychologyMedical educationMathematics educationMedicinePsychotherapistPhysicsPsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.052
GPT teacher head0.419
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicSimulation-Based Education in HealthcareFrench-language works237,207