The ways social networks shape reflection on early significant clinical experiences in medical school
Bibliographic record
Abstract
Background: Medical curricula are increasingly providing opportunities to guide reflection for medical students. However, educational approaches are often limited to formalized classroom initiatives where reflection is prescriptive and measurable. There is paucity of literature that explores the personal ways students may experience authentic reflection outside of curricular time. The purpose of this study was to understand how social networks might shape dimensions of reflection. Methods: This study employed a qualitative social network analysis approach with a core sample of seven first year undergraduate medical students who described their relationships with 61 individuals in their networks. Data consisted of participant generated sociograms and individual semi-structured interviews. Results: Many learners struggled to find significant ways to involve their social networks outside of medicine in their new educational experiences. It appeared that some medical students began in-grouping, becoming more socially exclusive. Interestingly, participants emphasized how curricular opportunities such as reflective portfolio sessions were useful for capturing a diversity of perspectives. Conclusions: Our study is one of the first to characterize the social networks inside and outside of medical school that students utilize to discuss and reflect on early significant clinical experiences. Recent commentary in the literature has suggested reflection is diverse and personal in nature and our study offers empirical evidence to demonstrate this. Our insights emphasize the importance of moving from an instrumental approach to an authentic socially situated approach if we wish to cultivate reflective lifelong learning.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".