‘Oh my God, I can't handle this!’: trainees’ emotional responses to complex situations
Bibliographic record
Abstract
CONTEXT: Dealing with emotions is critical for medical trainees' professional development. Taking a sociocultural and narrative approach to understanding emotions, we studied complex clinical situations as a specific context in which emotions are evoked and influenced by the social environment. We sought to understand how medical trainees respond to emotions that arise in those situations. METHODS: In an international constructivist grounded theory study, 29 trainees drew two rich pictures of complex clinical situations, one exciting and one frustrating. Rich pictures are visual representations that capture participants' perceptions about the people, situations and factors that create clinical complexity. These pictures were used to guide semi-structured, individual interviews. We analysed visual materials and interviews in an integrated way, starting with looking at the drawings, doing a 'gallery walk', and using the interviews to inform the aesthetic analysis. RESULTS: Participants' drawings depicted a range of personal emotions in response to complexity, and disclosed unsettling feelings and behaviours that might be considered unprofessional. When trainees felt confident, they were actively participating, engaged in creative problem-solving strategies, and emphasised their personal involvement. When trainees felt the situation was beyond their control, they described how they were running away from the situation, hiding themselves behind others or distancing themselves from patients or families. CONCLUSIONS: A sense of control seems to be a key factor influencing trainees' emotional and behavioural responses to complexity. This is problematic, as complex situations are by their nature emergent and dynamic, which limits possibilities for control. Following a social performative approach to emotions, we should help students understand that feeling out of control is an inherent property of participating in complex clinical situations, and, by extension, that it is not something they will 'grow out of' with expertise.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".