Students' social networks are diverse, dynamic and deliberate when transitioning to clinical training
Bibliographic record
Abstract
Abstract Context Transitions in medical education are dynamic, emotional and complex yet, unavoidable. Relationships matter, especially in times of transition. Using qualitative, social network research methods, we explored social relationships and social support as medical students transitioned from pre‐clinical to clinical training. Methods Eight medical students completed a social network map during a semi‐structured interview within two weeks of beginning their clinical clerkships (T 0 ) and then again four months later (T 1 ). They indicated meaningful interactions that influenced their transition from pre‐clinical to clinical training and discussed how these relationshipsimpacted their transition. We conducted mixed‐methods analysis on this data. Results At T 0 , eight participants described the influence of 128 people in their social support networks; this marginally increased to 134 at T 1 . People from within and beyond the clinical space made up participants’ social networks. As new relationships were created (eg with peers and doctors), old relationships were kept (eg with doctors and family) or dissolved over time (eg with near‐peers and nurses). Participants deliberately created, kept or dissolved relationships over time dependent on whether they provided emotional support (eg they could trust them) or instrumental support (eg they provided academic guidance). Conclusions This is the first social networks analysis paper to explore social networks in transitioning students in medicine. We found that undergraduate medical students’ social support networks were diverse, dynamic and deliberate as they transitioned to clerkships. Participants created and kept relationships with those they trusted and who provided emotional or instrumental support and dissolved relationships that did not provide these functions.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".