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Record W1997177573 · doi:10.3402/meo.v19.25181

What information is provided in transcripts and Medical Student Performance Records from Canadian Medical Schools? A retrospective cohort study

2014· article· en· W1997177573 on OpenAlexaffabout
Jason A. Robins, Matthew D. F. McInnes, Kaisra Esmail

Bibliographic record

VenueMedical Education Online · 2014
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsDocumentationStandardizationInclusion (mineral)Medical educationRetrospective cohort studyMedicineLimitingMedical recordQuality (philosophy)Family medicineCohortPsychologyActuarial scienceComputer sciencePathologyBusinessInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Resident selection committees must rely on information provided by medical schools in order to evaluate candidates. However, this information varies between institutions, limiting its value in comparing individuals and fairly assessing their quality. This study investigates what is included in candidates' documentation, the heterogeneity therein, as well as its objective data. METHODS: Samples of recent transcripts and Medical Student Performance Records were anonymised prior to evaluation. Data were then extracted by two independent reviewers blinded to the submitting university, assessing for the presence of pre-selected criteria; disagreement was resolved through consensus. The data were subsequently analysed in multiple subgroups. RESULTS: Inter-rater agreement equalled 92%. Inclusion of important criteria varied by school, ranging from 22.2% inclusion to 70.4%; the mean equalled 47.4%. The frequency of specific criteria was highly variable as well. Only 17.7% of schools provided any basis for comparison of academic performance; the majority detailed only status regarding pass or fail, without any further qualification. CONCLUSIONS: Considerable heterogeneity exists in the information provided in official medical school documentation, as well as markedly little objective data. Standardization may be necessary in order to facilitate fair comparison of graduates from different institutions. Implementation of objective data may allow more effective intra- and inter-scholastic comparison.

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)0.002
metaresearch head score (Gemma)0.033
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0350.000

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 head0.010
GPT teacher head0.343
Teacher spread0.333 · 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 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".

Quick stats

Citations6
Published2014
Admission routes2
Has abstractyes

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