Intended and unintended test constructs in a Multiple-Mini admission Interview. A validity study.
Bibliographic record
Abstract
Admission interviews in higher education may be developed with the intention to select applicants with specific personal competences not captured by traditional grade-based admission. In this study, we examined whether the data structure of multiple-mini admission interview scores supported the presence of communication, empathy, collaboration, and resilience as independent test dimensions. In addition, the associations between the interview scores and unintended test constructs (station format, pre-university grades, age, gender) were examined. Confirmatory and exploratory factor analyses and regression analyses were used to examine interview data from a cohort of Danish medical school applicants. The proposed multi-dimensionality was not supported by the data structure. The influence of the unintended constructs examined was limited or non-existing. These results are in line with the scarce existing literature. This situation makes a priori claims that the multiple-mini interview can measure multi-dimensional personal competences inadvisable, and care should be taken about what is communicated to stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".