ArticleBMC medical education2022
Assessing distinguishable social skills in medical admission: does construct-driven development solve validity issues of situational judgment tests?
Article in BMC medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Integrating formative and summative feedback in online situational judgement tests: effects of feedback design on medical students' motivational and cognitive learning factors.Medical education online · 2026Trial
- A multidimensional measure of pre-service teachers' perceived technological competence for teaching: integrating TPACK, DigCompEdu, and self-efficacy.Frontiers in psychology · 2026Article
- Predicting undergraduate OSCE performance using traditional and construct-driven situational judgment tests at admission.Advances in health sciences education : theory and practice · 2025Article
- Defining competence profiles in obstetrics and gynecology using the modified requirement tracking questionnaire.BMC medical education · 2025Article
- Article
- The effects of language proficiency and awareness of time limit in animated vs. text-based situational judgment tests.BMC medical education · 2024Article
- Measuring personal characteristics in applicants to German medical schools: Piloting an online Situational Judgement Test with an open-ended response format.GMS journal for medical education · 2024Article
- Physicians' Social Skills - Conceptualization, Taxonomy, and Behavioral Assessment.Perspectives on medical education · 2024Article
- Predicting Actual Social Skill Expression from Personality and Skill Self-Concepts.Journal of Intelligence · 2022Article
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Authors and funding
5 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial skills are important for future physicians and are therefore increasingly considered in selection processes. One economic assessment method from which different social skills can be inferred are Situational Judgment Tests (SJTs) in which applicants are asked to rate behavioral responses in context-relevant situations. However, traditional SJTs have so far failed to distinctively measure specified constructs. To address this shortcoming in the medical admission context, we applied a construct-driven approach of SJT development in which test development was deductively guided by agency and communion as target constructs.
methodThe final version of the construct-driven SJT includes 15 items per construct with three behavioral responses. Medical school applicants (N = 1527) completed the construct-driven SJT, a traditional SJT, and an aptitude test under high-stakes condition as part of their application. A subsample (N = 575) participated in a subsequent voluntary online study with self-report measures of personality and past behavior.
resultsThe proposed two-factor structure and internal consistency of the construct-driven SJT was confirmed. Communal SJT scores were positively associated with self-reported communal personality and communal behavior, yet effects were smaller than expected. Findings for agentic SJT scores were mixed with positive small associations to self-reported agentic personality scores and agentic behavior but unexpected negative relations to communal self-reported measures.
conclusionsResults suggest that construct-driven SJTs might overcome validity limitations of traditional SJTs, although their implementation is challenging. Despite first indicators of validity, future research needs to address practical points of application in high-stakes settings, inclusion of other constructs, and especially prediction of actual behavior before the application of construct-driven SJTs for selection purposes in medical admission can be recommended.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.