Admissions experiences of aspiring physicians from low‐income backgrounds
Bibliographic record
Abstract
INTRODUCTION: Students from low-income backgrounds (LIB) have been under-represented in Canadian medical schools for over fifty years. Despite our awareness of this problem, little is known about the experiences of aspiring physicians from LIB in Canada who are working towards medical school admission. Consequently, we have little insight into the barriers and facilitators that may be used to increase the representation of students from LIB in Canadian medical schools. METHODS: This paper describes a qualitative description interview study aimed at understanding the experiences of aspiring physicians from LIB as they attempt to gain entry to medical school. We conducted semi-structured interviews with 21 participants at different stages of their undergraduate, master's, and non-medical professional education, and used the theories of intersectionality and identity capital as a theoretical framework for identifying barriers and facilitators to a career in medicine. RESULTS: Participants experienced social, identity-related, economic, structural and informational barriers to a career in medicine. Intrinsic facilitators included motivation, self-confidence, attitude, strategy, information-seeking and sorting, and financial literacy and increasing income. Extrinsic facilitators were social, informational, financial and institutional in nature. CONCLUSION: This study fills existing knowledge gaps in the literature by identifying the pre-admissions barriers and facilitators encountered by aspiring physicians from LIB in Canada. The barriers and facilitators outlined in this study offer a framework for identifying target areas in developing support for admitting medical students from LIB. Given that medical students from LIB are more likely to serve underserved populations, our study is relevant to Canadian medical schools' social accountability commitment to producing physicians that meet the health needs of marginalised and vulnerable patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".