The Struggle to Belong for Underrepresented Medical Students: A Narrative Review
Bibliographic record
Abstract
Introduction: The concept of belonging (i.e., the feeling of connectedness with the people and environment around you) may, at first glance, seem straightforward and aspirational. However, there is a dark side to belonging when it becomes a driver to conform to unspoken and socially enforced group expectations. Applying critical sociological theory of the personal and political of belonging to undergraduate medical education, we asked: what are the experiences of belonging for underrepresented medical students? Methods: We conducted a narrative review of 16 qualitative research articles. We included papers published up to October 2024 that studied underrepresented medical students, defined as students who have been historically marginalized in medicine. Analysis involved interpreting students' experiences of personal and political belonging and the consequences of the interplay between these two dimensions. Results: Underrepresented medical students perceived a marked division between themselves and their peers-who they described as 'a different breed.' They saw medicine as an 'exclusive club' where their differences were highly visible and felt excluded from activities that were considered foundational for peer bonding. A lack of understanding from, and commonalities with, other students, as well as experiences with prejudice and social exclusion within the curriculum and from educators, negatively impacted students' ability to feel at home in medical school. Discussion: Our review highlights underrepresented medical students' struggle to belong in medical school. This struggle is not simply a matter of personal connection; it is rooted in systemic oppression and can have profoundly harmful impacts, ultimately influencing professional identity formation, choice of specialty, and well-being.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".