Can simulation foster resilience in medical students
Bibliographic record
Abstract
Resilience is considered to be ‘a mindset and skill set that can be nurtured into a stronger and more effective attribute’.1 Whether and how it can be nurtured in medical students is a subject of interest for medical educators.1 2 Little is known about how physicians develop resilience.3 While some interventions show promise,4 resilience training in medical education is not well studied. We aimed to develop a teaching intervention with high acceptability to undergraduate medical students, which would allow exposure to challenges in a controlled, psychologically safe environment, and might enhance their resilience. Simulation-based education provided opportunities for carefully designed scenarios and debriefing by trained facilitators. Structured debriefing enabled participants to recognise and discuss stressful situations, as well as increase their connection with each other and with their teachers. These factors have been found to enhance resilience in other contexts.5 Participants’ impressions were explored qualitatively, and suggest that simulation can encourage reflection on the non-technical skill of resilience, provided there is careful design and debriefing of the simulation activity. In this project we sought to understand whether simulation can be used as a tool to explore and enhance resilience in medical students. Enhancing resilience is recognised as a worthy goal for a variety …
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".