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Record W2066645039 · doi:10.3109/0142159x.2013.857012

Effect of the Bologna bachelor degree on considerations of medical students to interrupt or terminate their medical training

2013· article· en· W2066645039 on OpenAlexaboutno aff
Sjoukje van den Broek, Olle ten Cate, Marjo Wijnen‐Meijer, Marijke van Dijk

Bibliographic record

VenueMedical Teacher · 2013
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorMedical educationBachelor degreeInterruptMedicineQuarter (Canadian coin)PsychologyNursingEngineeringPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The bachelor-master system potentially enables medical students to stop or temporary interrupt their training after obtaining a bachelor degree. A survey at the time of introduction of this two-cycle model in Dutch medical education showed little interest among students in these possibilities. AIMS: To investigate students' considerations to stop or pause now that this model is well established. METHODS: Questionnaires were sent to 314 second year and 348 third year bachelor students and 256 first year master students at University Medical Center Utrecht. RESULTS: Response rates were 33.4% for the second year and 42.0% for the third year bachelor students and 48.8% for the master students. Of all these students, one to three percent seriously considered a permanent stop. Of the bachelor students, about one quarter seriously considered a temporary stop after finishing the bachelor program. Of the master students, one in seven indicated that they did take a break at that opportunity. CONCLUSIONS: Awarding the bachelor degree does not particularly encourage students to discontinue their medical study. Our results are comparable to the results of the survey at the time of the introduction of the bachelor-master system, which supports our previous conclusion.

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)0.005
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.102
GPT teacher head0.432
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations8
Published2013
Admission routes1
Has abstractyes

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