First Nations People: Addressing the Relationships between Under-Enrollment in Medical Education, STEM Education, and Health in the United States
Bibliographic record
Abstract
In the United States of America, an analysis of enrollment statistics to institutions of higher education, those pursuing Science, Technology, Engineering, and Mathematics (STEM) fields, as well as those pursuing medical education show a paralleled ethnic stratification. Based upon such stratification, Native Americans consistently rank amongst the lowest demographic groups to enroll in and pursue higher education, STEM or medical education. A perturbed history of the First Nations people in the establishment of the United States of America laid the foundation for a multitude of factors contributing to current trends in health, living, and academic pursuits amongst First Nation’s people. This paper aims to explore the factors underlying the lack of Native American enrollment in higher education, careers in STEM and medicine. An investigation was conducted following a broad literature review relevant to the topic, and articles were critically appraised using the Search, Appraisal, Synthesis of Analysis (SALSA) framework as well as the Standards for Reporting Qualitative Research (SRQR). Findings from such studies indicate that the Native American communities face a unique set of social circumstances rooted in a historical context, with several unmet basic needs of living required for integration, access, and pursuit of higher education.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".