Low-income Barriers and Facilitators to a Career in Medicine
Bibliographic record
Abstract
INTRODUCTION: Students from low-income backgrounds (LIB) have been underrepresented 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. As a result, we do not have insight into the barriers and facilitators that may be used to increase the representation of students from LIB in Canadian medical schools. METHODS: This thesis 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 15 participants at different stages of their undergraduate, master’s, and non-medical professional education. RESULTS: We used the theories of intersectionality and identity capital as a theoretical framework for identifying barriers and facilitators to a career in medicine. 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 gaps in the literature by identifying the pre-admissions barriers and facilitators encountered by aspiring physicians from LIB. This information will be useful to medical schools, organizations, and researchers interested in supporting underrepresented groups. Given that medical students from LIB are more likely to serve underserved populations, this is relevant to Canadian medical schools’ social accountability commitment to producing physicians that meet the health needs of marginalized and vulnerable patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.259 | 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 teacher head, 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".