The postgraduate medical educational climate assessed by the Danish Residency Educational Climate Test (DK-RECT): a validation and cross-sectional observational study

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Background: A good educational climate is essential for delivering high-quality training for medical trainees, professional development, and patient care. The aim of this study was to (1) validate the Dutch Residency Educational Climate Test (D-RECT) in a Danish setting and (2) describe and evaluate the educational climate among medical trainees. Methods: D-RECT was adopted in a three-step process: translation of D-RECT into Danish (DK-RECT), psychometric validation, and evaluation of educational climate. Trainees from 31 medical specialties at Copenhagen University Hospital – Rigshospitalet, Denmark were asked to complete an online survey in a cross-sectional study. Results: We performed a forward-backward translation from Dutch to Danish. Confirmatory factor analysis showed that DK-RECT was robust and valid. The reliability analysis showed that only seven trainees from one specialty were needed for a reliable result. With 304 trainees completing DK-RECT, the response rate was 68%. The subsequent analysis indicated a positive overall educational climate, with a median score of 4.0 (interquartile range (IQR): 3.0–5.0) on a five-point Likert scale. Analysis of the subscales showed that the subscale Feedback received the lowest ratings, while Supervision and Peer collaboration were evaluated highest. Conclusions: Psychometric validation of D-RECT in a Danish context demonstrated valid results on the educational climate in specialist training. DK-RECT can be used to evaluate the effectiveness of interventions in the future and can facilitate the conversation on the educational climate.

Original languageEnglish
Article number943
JournalBMC Medical Education
Issue number1
Number of pages13
Publication statusPublished - 2023

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Publisher Copyright:
© 2023, The Author(s).

    Research areas

  • Clinical competence, Education, Educational, Factor analysis, Graduate, Medical, Models, Psychometrics, Statistical

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