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Adaptação transcultural do questionário EFFECT para português brasileiro

2021· article· en· W4230074409 on OpenAlexaboutno aff
Lourrany Borges Costa, Shamyr Sulyvan de Castro, Diovana Ximenes Cavalcante Dourado, Bruna Soares Praxedes, Thayná Custódio Mota, Thais Marcella Rios de Lima Tavares

Bibliographic record

VenueRevista Brasileira de Educação Médica · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaPreceptorContext (archaeology)Medical educationPortugueseCurriculumTest (biology)PsychologyBrazilian PortugueseAdaptation (eye)Reliability (semiconductor)Consistency (knowledge bases)PedagogyComputer scienceMedicinePsychometricsClinical psychologyLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract: Introduction: Clinical teaching is based on a real work environment, in professional practice settings, such as health services and units, under the supervision of the preceptor. Providing medical teachers with an assessment of their teaching skills is a powerful tool for improving clinical learning for students in training. In this context, the EFFECT (Evaluation and Feedback for Effective Clinical Teaching) questionnaire was developed by Dutch researchers in 2012 for teacher evaluation, being validated based on the literature about medical teaching in the workplace and incorporates the skills of the Canadian competency-based medical curriculum. Objective: To translate and cross-culturally adapt into Brazilian Portuguese and to validate the EFFECT questionnaire for teacher evaluation by Medical students. Method: Cross-cultural adaptation with the following steps: initial translation of the English version, synthesis of translated versions, back-translation, creation of a consensual version in Brazilian Portuguese, with adaptation, review, and analysis of content validity by an expert committee, pre-test with retrospective clarification interview, and reliability analysis by factorial analysis and internal consistency test (Cronbach’s alpha coefficient). Result: In the translation and back-translation stages, the disagreements were related to the use of synonyms and none of the items were modified in terms of their understanding, but in terms of adaptation into the Brazilian context. The evaluation of the expert committee showed the versions maintained the semantic and idiomatic equivalences of the content. Eighty-nine students participated in the pre-test. The internal consistency of the EFFECT questionnaire in Brazilian Portuguese was excellent for all domains, with Cronbach’s alpha coefficient ranging from 0.82 to 0.94. Conclusion: The translated and adapted version of the EFFECT questionnaire into Brazilian Portuguese is equivalent to the original instrument and has evidence of high validity and reliability, being able to constitute a national tool to evaluate the efficiency of clinical medicine teaching.

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)0.038
metaresearch head score (Gemma)0.082
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.082
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.026
GPT teacher head0.343
Teacher spread0.317 · 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
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".

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Citations2
Published2021
Admission routes1
Has abstractyes

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