A construct validity study of clinical competence: A multitrait multimethod matrix approach
Bibliographic record
Abstract
INTRODUCTION: The purpose of the study was to adduce evidence for estimating the construct validity of clinical competence measured through assessment instruments used for high-stakes examinations. METHODS: Thirty-nine international physicians (mean age = 41 + 6.5 y) participated in high-stakes examination and 3-month supervised clinical practice to determine the practice readiness of physicians. Three traits-doctor-patient relationship, clinical competence, and communication skills-were assessed with objective structured clinical examinations, in-training evaluation reports, and clinical assessments. These traits were intercorrelated in a multitrait multimethod matrix (MTMM). RESULTS: The reliability of assessments ranged from moderate to high (Cronbach's alpha: 0.58-0.98; Ep(2) = 0.79). There is evidence for both convergent and divergent validity for clinical competence, followed by doctor-patient relationships, and communications (validity coefficients = 0.12-0.85). The correlations between the same methods but different traits indicate that there is substantial method specificity in the assessment accounting for nearly one-quarter of the variance (23.7%). DISCUSSION: There is evidence for the construct validity of all 3 traits across 3 methods. The MTMM approach, currently underutilized, could be used to estimate the degree of evidence for validating complex constructs, such as clinical competence.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".