Assessing noncognitive domains of respiratory therapy applicants: Messick’s framework appraisal of the multiple mini-interview
Bibliographic record
Abstract
Educators who assess incoming applicants into a health professional training program are looking for a wide array of cognitive and noncognitive skills that best predict success in the program and as a future practicing professional. While aptitude tests generally measure cognitive skills, noncognitive constructs are more difficult to measure appropriately. The traditional method of measuring noncognitive constructs has been the panel interview. Panel interviews have been described as inconsistent in measuring noncognitive domains and consistently reported as unreliable and susceptible to bias. An alternate interview method used in many health professions schools is the multiple mini-interview (MMI) that was specifically designed to assess noncognitive domains in health professions education. This paper discusses the purpose of using the MMI, how the MMI is conducted, specific domains of focus for the MMI, and the feasibility of creating an MMI. Finally, the paper uses Messick's framework on validity to guide the consideration of the MMI.
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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 | 0.001 | 0.002 |
| 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".