Stretching the Comfort Zone: Using Early Clinical Contact to Influence Professional Identity Formation in Medical Students
Bibliographic record
Abstract
PURPOSE: To explore first-year medical students' affective reactions to intimate encounters with severely sick patients in their homes, within a curricular innovation targeting the development of a patient-centered professional identity. BACKGROUND: Early patient encounters create complex emotional challenges and constitute fertile ground for professional identity formation. The literature indicates that students often learn, largely through the hidden curriculum, to avoid and suppress emotion. This can culminate in mental health problems and loss of empathy. METHOD: A qualitative descriptive analysis of 28 randomly selected, mandatory, reflective essays focused on a home visit to a previously unknown patient, in an unsupervised group of 4 students, within the context of a structured course called Patient Contact-PASKON. RESULTS: Students described a wide range of affect-laden responses, positive and negative, elicited by the home visits. The observations were typically related to loss of control, struggles to behave "professionally," and the unmasking of stereotypes and prejudices. CONCLUSIONS: Medical students' initial clinical encounters elicit emotional responses that have the potential to serve as triggers for the development of emotional maturity, relational skills, and patient-centered attitudes. Conversely, they can foreground uncertainty and lead to defensive distancing from patients' existential concerns. The findings point to a role for structured educational strategies and supervision to assist students in the emotion work necessary in the transition from a "lay" to a "medical" identity.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".