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Record W2531188243 · doi:10.1097/acm.0000000000001448

Avoiding Common Data Analysis Pitfalls in Health Professions Education Research

2016· article· en· W2531188243 on OpenAlexaff
Jimmie Leppink, Kulamakan Kulasegaram

Bibliographic record

VenueAcademic Medicine · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHealth professionsMedical educationAcademic medicineMedicineFamily medicinePolitical scienceHealth care

Abstract

fetched live from OpenAlex

Avoiding Common Data Analysis Pitfalls in Health Professions Education Research Jimmie Leppink;Kulamakan Kulasegaram; Academic Medicine

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)0.740
metaresearch head score (Gemma)0.912
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.260
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7400.912
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0120.015
Science and technology studies0.0080.032
Scholarly communication0.0150.027
Open science0.0080.017
Research integrity0.0130.035
Insufficient payload (model declined to judge)0.0040.001

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 head0.353
GPT teacher head0.486
Teacher spread0.133 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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