Identifying factors influencing program selection in health sciences by underrepresented minority students—a scoping review
Bibliographic record
Abstract
There is a critical need to diversify health professionals to better serve the growing minority population in North America. Unfortunately, minority groups remain underrepresented in health professions. Despite recruitment efforts by government and academic institutions, fewer underrepresented minority (URM) students choose careers in healthcare. Identifying the key factors influencing URM students’ decisions to pursue health sciences programs could enhance diversity in these programs through targeted admissions strategies, ultimately leading to a more diverse future healthcare workforce. This scoping review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews. Five electronic databases and gray literature were searched to identify North American papers published between 1942 and 2022. Identified studies focused on URM students’ perceptions of facilitators and barriers to matriculate into a health science degree. Twenty-one articles were analyzed. Following content analysis, the facilitators and barriers identified comprised personal, socio-cultural, institutional, and financial. The most frequent facilitators reported were scholarships, family support, and the presence of role models. Common barriers included high tuition fees, pre-admission criteria, lack of awareness about the health profession, availability of financial aid, and the admission process. The findings of this review will facilitate the development and implementation of customized, comprehensive strategies to recruit more URM students to health science programs in the future, thereby improving efforts toward creating a diverse healthcare workforce.
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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 | 0.031 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".