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Record W7028035490

DOES MAJOR MATTER? AN EXAMINATION OF UNDERGRADUATE MAJOR AND MEDICAL SCHOOL ADMISSION

2021· other· en· W7028035490 on OpenAlexaboutno aff

Bibliographic record

VenueTUScholarShare (Temple University) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEntrance examCLARITYMedical schoolConstruct (python library)Flexibility (engineering)CurriculumInstitutionLogistic regression
DOInot available

Abstract

fetched live from OpenAlex

The official stance of the Association America of Medical Colleges (AAMC) regarding the undergraduate major of applicants for admission to medical school is that there are no required or preferred majors. While the AAMC is the body that governs admission to allopathic medical schools in the United States, this statement does not provide clarity to prospective medical school applicants as to what undergraduate major to select; it only encourages students from a variety of educational backgrounds to apply. Furthermore, a broad statement about undergraduate major flexibility does not indicate how choice of major will eventually impact admission to medical school. While the AAMC encourages applicants to choose any undergraduate major they wish, there is minimal peer-reviewed research or empirical evidence of the relationship between applicants' undergraduate major and their likelihood of admission to medical school.
\nThrough the lens of the student-choice construct, this dissertation sought to determine if applicants' undergraduate major is a statistically significant predictor of successful admission to medical school. This model accommodates decisions such as the intent to pursue post-secondary education, which institution to attend, what major to choose, and whether to persist to degree completion. The student-choice construct also contends that these decisions are influenced by the amount of human, financial, social, and cultural capital available to the student throughout the decision-making process.
\nTo study how choice of major impacts admission to medical school, I conducted a quantitative study using a hierarchical binary logistic regression. Secondary data were collected using the formal data request procedure outlined by the AAMC. Application-level data were received from the AAMC, and personally identifiable information including applicants’ names, identification numbers, and addresses were removed by the AAMC before the data were delivered. Additionally, given that the study involves the analysis of de-identified extant data, this study received exemption from the Institutional Review Board at Temple University. The dataset included 53,371 applicants to allopathic medical school for the 2019 application cycle. These applicants attended undergraduate institutions primarily located in the United States and Canada.
\nThe study revealed that undergraduate major does not serve as a statistically significant predictor of admission to medical school over and above applicants' demographic characteristics, MCAT scores, and undergraduate grade point average. Applicants who chose a Biology, Chemistry, Physics, or Mathematics (BCPM) major did not have a greater chance of being admitted to medical school than an applicant who chose a non-BCPM major. These findings are consistent with previous studies that sought to predict variables that contribute to medical school admission.
\nFuture research should investigate the predictive ability of admissions variables such as applicant characteristics captured from medical school interviews; letters of recommendation; personal statements and community service, leadership, and healthcare experiences. A combined or comparative study similarly analyzing applicants to different health profession programs might also be useful. In addition, a non-binary categorization of specific undergraduate majors would provide an even more nuanced analysis of how different majors predict admission to medical school.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.017
GPT teacher head0.251
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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