From observation to ownership: a qualitative study of medical students’ learning under distant clinical supervision
Bibliographic record
Abstract
OBJECTIVES: This qualitative study explores the experiences of medical students involved in clinical work and learning under distant supervision, aiming to understand their adaptation, challenges and learning processes in the context of clinical uncertainty and reduced oversight. DESIGN: This study employed a constructivist grounded theory (CGT). CGT was chosen for its strength in examining complex social interactions and uncovering emergent themes that are not fully explained by existing theoretical frameworks. Data were collected through 13 semi-structured, in-depth interviews with medical students who actively participated in clinical care under conditions of limited supervision and high responsibility. SETTING: Faculty of Medicine, Switzerland. PARTICIPANTS: We conducted interviews with 13 medical students who worked in Mobile SWAB Teams during the COVID-19 pandemic. RESULTS: Students described a shift from observation to actively taking on a professional role. This experience provided a unique opportunity for medical students to apply their knowledge and skills in real-world settings, develop a sense of autonomy and foster personal growth. Acknowledging the importance of effective communication, teamwork and decision-making in providing patient care, they embraced the concept of self-regulated learning (SRL). CONCLUSIONS: Creating a supportive learning environment that promotes SRL encourages collaboration and enables medical students to take on clinical tasks with increasing autonomy. In our study, working under distant supervision promoted reflection, strengthened communication and supported both clinical development and identity formation. This approach highlights the value of integrating supported responsibility and guided reflection into future models of clinical education.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".