A multifaceted early clinical experience course in internal medicine fosters motivation and professional growth from the perspective of first-year medical students
Bibliographic record
Abstract
BACKGROUND: Early clinical experience within a vertically integrated curriculum might contribute to the development of the desired competencies from the onset of studying medicine. However, most qualitative studies focused on the effects of early clinical experience on students were performed within a primary care setting in the second and third year of studies. Our aim was to explore, from the perspective of first-year medical students, the effects of an early clinical experience course in internal medicine within a tertiary hospital setting on their professional and personal development. METHODS: We used an inductive approach to conduct a conventional content analysis of 27 reflective writing reports written by first-year medical students after having completed a 60-hour early clinical experience course in the inpatient setting of a university hospital, comprising 48 h in the healthcare setting (primarily internal medicine and its subspecialties) and 12 h in team-building social events. Writing reports aimed to make students openly reflect on their course experience, elaborating on any aspect of perceived relevance. RESULTS: All 27 students invited to participate wrote a reflective report. We identified three themes with their respective categories of codes: (1) Professional growth, including formation of professional identity, dealing with emotions and experience with death; (2) Reinforcing motivation for further studies and work as a physician, including integration into medical studies, shaping a supporting environment and course as a highlight of the studies; and (3) Immersion into the medical field based on real-world exposure, including benefitting from early patient contact and exploration of the field of internal medicine. Throughout the reflective reports, role modelling appeared repeatedly as a driving element for the observed effects. CONCLUSIONS: Our findings suggest that, from the perspective of first-year medical students, participation in an early clinical experience course in internal medicine within a tertiary hospital setting positively influences their initiation into professional growth, motivation for further studies and work as physician, and immersion in the medical profession. Further studies are needed to better understand the underlying success factors of such courses.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".