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Record W2109020245 · doi:10.1080/01421590500078471

A modified electronic key feature examination for undergraduate medical students: validation threats and opportunities

2005· article· en· W2109020245 on OpenAlexaboutno aff
Martin R. Fischer, Veronika Kopp, Matthias Holzer, Franz Ruderich, Jana Jünger

Bibliographic record

VenueMedical Teacher · 2005
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaTest (biology)Reliability (semiconductor)GermanMedical educationPsychologyKey (lock)Feature (linguistics)Computer scienceMedicinePsychometricsClinical psychologyComputer security

Abstract

fetched live from OpenAlex

The purpose of our study was the development and validation of a modified electronic key feature exam of clinical decision-making skills for undergraduate medical students. Therefore, the reliability of the test (15 items), the item difficulty level, the item-total correlations and correlations to other measures of knowledge (40 item MC-test and 580 items of German MC-National Licensing Exam, Part II) were calculated. Based on the guidelines provided by the Medical Council of Canada, a modified electronic key feature exam for internal medicine consisting of 15 key features (KFs) was developed for fifth year German medical students. Long menu (LM) and short menu (SM) question formats were used. Acceptance was assessed through a questionnaire. Thirty-seven students from four medical schools voluntarily participated in the study. The reliability of the key feature exam was 0.65 (Cronbach's alpha). The items' difficulty level scores were between 0.3 and 0.8 and the item-total correlations between 0.0 and 0.4. Correlations between the results of the KF exam and the other measures of knowledge were intermediate (r between 0.44 and 0.47) as well as the learners' level of acceptance. The modified electronic KF examination is a feasible and reliable evaluation tool that may be implemented for the assessment of clinical undergraduate training.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.377
Teacher spread0.325 · 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 designOther design
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

Citations86
Published2005
Admission routes1
Has abstractyes

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