Medical Students’ Perceptions on Identifying and Addressing Emotional Responses in Emergency Medicine: Pilot Investigation
Bibliographic record
Abstract
BACKGROUND: Training in acute care, such as emergency medicine (EM), where exposure to critically ill and injured patients is high, impacts the well-being of trainees and contributes to burnout. Investigating how, and if, trainees prepare for these situations is necessary to ensure they are supported. OBJECTIVE: This study aimed to evaluate medical students' perspectives and emotional preparedness for handling acute care and trauma. METHODS: We conducted a pilot investigation using a remote digital survey of medical students during their EM clerkship at a large, urban academic institution. The primary outcome of interest was student-reported preparedness and comfort in handling trauma and critical care patient encounters. Secondary outcomes included awareness of well-being resources and comfort in accessing digital well-being resources. RESULTS: A total of 57 medical students completed the voluntary digital survey, and half of the students (n=28, 49%) reported having witnessed the care of a critically ill or a penetrating trauma patient (eg, a victim of gun violence). A majority (n=40, 70%) had thought about how these events may impact them, and over half felt unprepared to identify the emotional impact these cases may have on them (n=31, 54%) or address the emotional or mental health impact (n=36, 63%). Less than a quarter (n=14, 25%) were aware of digital mental health resources, and 58% (n=33) did not feel fully comfortable connecting with resources if needed. Students who had previously witnessed critical care were significantly more likely to report feeling well prepared in identifying the emotional impact and addressing this impact. CONCLUSIONS: In this cross-sectional survey, students did not feel fully prepared to identify or address the emotional impact of working in EM. Additionally, they lacked awareness of or comfort with accessing digital institutional resources meant to support their well-being, such as a large web-based platform. These findings can help inform and guide interventions by educational and academic leaders. The aim would be to create and promote environments that empower students with tools to identify their own emotions and connect to well-being resources.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".