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Record W4385665437 · doi:10.36834/cmej.76255

A brief report of aspiring medical student perceptions and behaviours concerning research experiences for selection into Canadian medical schools

2023· article· en· W4385665437 on OpenAlexaffvenueabout
Irene Chang, Laurie Yang, Asiana Elma, Stacey A. Ritz, Lawrence Grierson

Bibliographic record

VenueCanadian Medical Education Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsMcMaster University Medical CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedical educationPerceptionMedical schoolPresentation (obstetrics)Selection (genetic algorithm)PsychologyMedical researchMedicineFamily medicineComputer science

Abstract

fetched live from OpenAlex

Background: Aspiring medical students behave based on their perception of what is valued in the selection process. While research experience is not explicitly considered in most Canadian admissions policies, it is commonly held as valuable within aspiring medical student communities. The purpose of this study is to describe the perceptions and behaviours of aspiring medical students with respect to gaining research experience in support of their medical school applications. Methods: We surveyed prospective applicants of Canadian medical schools between August 2021 and November 2021, then compiled descriptive statistics pertaining to their perceptions and behaviours. Results: Respondents affirmed the belief that research experience is valued in medical school admissions processes. They reported spending approximately 13 hours per week engaged in research, which usually did not yield publication or presentation recognition. Conclusion: Aspiring medical students invest substantial time and energy in research experiences to benefit their applications. There is room for medical schools to be more transparent about the value of research experience in their admissions processes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.225
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.225
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1170.000

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.

Opus teacher head0.077
GPT teacher head0.492
Teacher spread0.415 · 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 teacher head, not a consensus.

Study designNot applicable
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".

Quick stats

Citations5
Published2023
Admission routes3
Has abstractyes

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