Demonstrating Construct Validity of the American Board of Physical Medicine and Rehabilitation Part I Examination: An Analysis of Dimensionality
Bibliographic record
Abstract
BACKGROUND: Ideally, high-stakes examinations assess 1 dimension of medical knowledge to produce precise estimates of a candidate's performance. It has not been reported whether the American Board of Physical Medicine and Rehabilitation Part 1 Certification Examination (ABPMR-CE-1) is unidimensional or not. OBJECTIVE: To examine the ABPMR-CE-1 to measure how many dimensions it assesses. DESIGN: Retrospective observational study. SETTING: We assessed examination results from the 2015 ABPMR-CE-1. PARTICIPANTS: A total of 489 deidentified candidates taking the 2015 ABPMR-CE-1. METHODS: A 1-parameter Item Response Theory (IRT) measurement model was utilized. A Principal Components Analysis (PCA) of standardized residual correlations was used to detect multidimensionality. MAIN OUTCOME MEASURE: Number of primary dimensions reflected in the 325 test questions. RESULTS: The results of the dimensionality analysis indicated that the ABPMR-CE-1 examination is highly unidimensional from a psychometric perspective. Expert content review of the substantive content of small contrasting clusters of questions provided additional assurance of the unidimensional nature of the examination. CONCLUSIONS: The ABPMR-CE-1 appears indeed to measure a single construct, which suggests a sound structure of the examination. It closely approximates the assumption of statistical unidimensionality. LEVEL OF EVIDENCE: Not applicable.
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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 | 0.081 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".