Evaluation of Longitudinal Assessment for Use in Maintenance of Certification
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to evaluate a longitudinal assessment program for physical medicine and rehabilitation (LA-PM&R) as a replacement for the American Board of Physical Medicine and Rehabilitation maintenance of certification examination. DESIGN: In this quality improvement study, randomly selected American Board of Physical Medicine and Rehabilitation diplomates were invited to participate in the LA-PM&R. Participants' maintenance of certification scaled scores were compared with LA-PM&R nonparticipants. We examined the association between LA-PM&R scores and maintenance of certification scaled scores and performance on clone items placed on both examinations. RESULTS: There were 2222 American Board of Physical Medicine and Rehabilitation diplomates eligible to take the maintenance of certification examination from February 2018 to February 2019. Invitations were sent to 1050 randomly selected diplomates, of whom 448 initially agreed. Three hundred eighteen individuals completed more than two quarters of LA-PM&R participation and took the maintenance of certification examination. Two hundred sixty-six diplomates took the maintenance of certification examination in the same period who did not participate in LA-PM&R. The LA-PM&R group scored higher on the maintenance of certification examination than the control group (P < 0.05). Performance on the two examinations were highly correlated, r = .50, P < 0.0001. On clone items, LA-PM&R participants had 74% correct on the LA-PM&R but 86% correct on the maintenance of certification examination (P < 0.01). CONCLUSIONS: The LA-PM&R program leads to better learning and retention of information than the 10-yr maintenance of certification examination.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".