Between agency and systemic barriers: Pathways to medicine and health sciences among Black students with immigrant parents from the Caribbean or Sub-Saharan Africa in Quebec, Canada
Bibliographic record
Abstract
This qualitative study, based on life stories, documents the pathways to medicine and health sciences of Black students with immigrant parents from the Caribbean or Sub-Saharan Africa in Quebec, Canada. The aim of this study is to investigate the factors that shape their educational pathways using Doray’s framework. Even among students from families with substantial levels of education, the educational pathways to medicine or health sciences may be described as non-linear. Several obstacles can arise along these pathways, depending on various social markers. Many of the interviewees first enroll in a program other than their desired program, either to ensure their financial security or to improve their grades for a limited-enrollment program. Medicine and pharmacy studies remain a dream for most participants and their parents. However, in some cases, this dream is not coming true, and interviewees’ aspirations are sometimes stifled. These results shed light on the possible changes to be made within certain programs’ admissions policies. Nevertheless, the students (n = 12) demonstrate agency in facing a seemingly unfair admissions system for highly selective programs. We conclude with recommendations on how to better accommodate the so-called non-traditional pathways of Black students with immigrant parents from the Caribbean or Sub-Saharan Africa.Practice pointsMost of the interviewees first enroll in a program other than their desired program (tiered approach), either to ensure their financial security or to improve their grades to get into a limited-enrollment program.The tiered approach that has been taken by the students indicates that universities should consider taking only the most recent grades into account during the selection process as well as non-academic life experiences.Access programs with support throughout students’ education remain an avenue for improving representation of students from underrepresented groups in medicine and other health programs.In future studies, it would be relevant to examine how pipeline programs can shape the pathways of students from underrepresented populations in health sciences, and how these students perceive the selection process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".