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Record W3024188083 · doi:10.5546/aap.2019.e425

Validación transcultural del Cuestionario de Enseñanza Clínica de Maastricht

2019· article· es· W3024188083 on OpenAlexaff
Sergio Giannasi, Eduardo Durante, Fernando Javier Vázquez, Claudia Kecskes, Roberta Ladenheim, Carlos Brailovsky

Bibliographic record

VenueArchivos Argentinos de Pediatria · 2019
Typearticle
Languagees
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCronbach's alphaConfirmatory factor analysisReliability (semiconductor)Variance (accounting)Adaptation (eye)PsychologyConstruct validityMedical educationContent validityStructural equation modelingClinical psychologyMedicinePsychometricsStatisticsMathematics

Abstract

fetched live from OpenAlex

The evaluation of the clinical teacher's performance provides feedback to motivate them to improve their teaching skills. To perform the cross-cultural adaptation of the Maastricht Clinical Teaching Questionnaire, the International Guide for the Adaptation of the Questionnaires was followed. The validity of content, response process, construct and reliability were investigated. After cross-cultural adaption, residents of two University hospitals evaluated 187 clinical teachers. Content and answering process were validated. In the confirmatory factor analysis, all indexes and criteria for a good fit suited the 5 factors and 14 items model. The Cronbach's alpha coefficient was 0.80. The G coefficient was > 0.70, with low variance of the absolute error. Every clinical teacher should receive at least 6 evaluations to achieve a reliable evaluation of every domain and of their global performance. The Spanish version of the questionnaire is a valid, reliable instrument for medical residents to evaluate teachers.

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)0.019
metaresearch head score (Gemma)0.044
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 score0.027
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.294
Teacher spread0.283 · 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".

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Citations2
Published2019
Admission routes1
Has abstractyes

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