Equity and Longitudinal Assessments: Perspectives from Physician Assistants/Associates (PAs) Participating in PANRE-LA
Bibliographic record
Abstract
Introduction: Longitudinal assessments (LAs) may offer more flexibility and unique opportunities to enhance equity. Although prior findings on LAs demonstrate that they foster learning, limited research exists on potential differences in examinee perspectives by demographics and practice characteristics. Addressing this research gap is vital to ensuring that examinees from different backgrounds equally derive learning benefits from LAs. Methods: We evaluated potential differences in perspectives and experiences of physician assistants/associates (PAs) participating in the PA National Recertifying Examination Longitudinal Assessment (PANRE-LA) program, considering a wide range of demographic and practice characteristics. Results: Over 90% agreed that this type of assessment provides a learning experience, helps to identify gaps, provides an opportunity to improve, aligns with a lifelong learning perspective, and keeps core medical knowledge up-to-date. Approximately 84% believed it helps them to be a better practitioner, and 78.7% either anticipated or had applied learning from PANRE-LA to their clinical practice. Discussion: Our findings suggest that PAs across diverse demographics and practice characteristics equally derive self-reported learning benefits from PANRE-LA. LAs, due to their formative components, may provide unique opportunities to promote equity in knowledge acquisition, foster continuous learning, and ultimately contribute to improved patient care.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".