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Record W4402400912 · doi:10.22454/fammed.2024.388919

Mini Med School: Knowledge and Resources for Underrepresented in Medicine Youth

2024· article· en· W4402400912 on OpenAlexaffabout
Kimberly M. Papp, Amanda R. Krysler, S. K. Lee, Shelley Ross

Bibliographic record

VenueFamily Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUnderrepresented MinorityMedical educationPsychologyFamily medicineMedicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Physician demographics in North America do not yet reflect the diversity of the communities they serve, accounted to systemic barriers targeting underrepresented in medicine (URiM) groups. URiM medical graduates are more likely to pursue generalist specialties, including family medicine. Mini Med Schools (MMSs) are pathway programs intended to motivate URiM youth to pursue medicine. A gap in literature exists regarding the potential of MMSs to provide youth with useful information. We examined the extent to which youth reported a change in knowledge about medicine as a career before and after attending an MMS. METHODS: Asclepius Medical Camp for Youth is a weeklong MMS for high school students, held at one Canadian university. In 2022, 50 youth participants were invited to complete surveys and quizzes measuring their knowledge about pursuing a career in medicine. RESULTS: The mean self-reported knowledge differed significantly precamp (n=34, M=5.87/10, SD=1.9) versus postcamp (n=26, M=8.28/10, SD=1.4; t[35]=7.07, P<.05). Likewise, participants' scores demonstrated a significant difference in mean scores precamp (n=43, M=7.12, SD=2.39) versus postcamp (n=39, M=9.31, SD=1.13; t[42]=5.08, P<.05). CONCLUSIONS: These findings highlight MMSs as a promising strategy to provide knowledge about medical careers beyond instilling motivation. By both inspiring and informing URiM youth, the long-term outcome of diversifying medicine may be achieved.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.617
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.381
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes2
Has abstractyes

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