Comparison of self, physician, and simulated patient ratings of pharmacist performance in a family practice simulator
Bibliographic record
Abstract
The Family Practice Simulator (FPS) was piloted as a teaching, learning, and assessment opportunity for pharmacists making the transition into primary care practice. During this one-day simulation of a typical day in a family physician's office, nine pharmacists rotated through a series of 13 OSCE stations where they interacted with physicians, patients, nurses and office staff while completing primary care activities and receiving performance evaluations. Pharmacists' performance ratings from self, physician, and standardized patient evaluations were compared using Global Rating Scales (GRS) scores and station-specific key points checklists. The mean (SD) overall GRS scores obtained by pharmacists across all stations in the FPS were 4.56 (SD = 0.60) from standardized patients, 3.95 (SD = 0.63) from physicians, and 3.60 (SD = 0.63) from self-assessment (out of a maximum score of 5). Agreement between pharmacists' and patients' GRS ratings ranged from moderate to good (generalizability coefficient (G) = 0.45 to 0.72) for all except one station. Agreement in GRS scores between pharmacists and physicians was at most fair for every station (G = 0.02 - 0.26). There was fair agreement on key points scores between pharmacists and patients (weighted kappa = 27%; 95% CI 7%, 47%) and moderate agreement between pharmacists and physicians (weighted kappa = 45%; 95% CI 21%, 70%). Although there was at best moderate agreement in rating scores between pharmacists, standardized patients, and physicians, the FPS provided an important opportunity to measure expectations regarding the professional role, responsibilities, and performance of pharmacists from a multi-professional perspective, thus better preparing pharmacists for integration into primary care practice. Differences in agreement may have been due to different preconceptions and expectations among raters.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".