Standardized Assessment of Pharmacists' Patient Care Competencies:
Bibliographic record
Abstract
Assessing the ongoing competence of practicing health care professionals requires regulators to balance complex demands of governments and the public, as well as interests and concerns of practitioners. A proliferation of models has evolved across professions and jurisdictions. In this article, we report on a model utilizing standardized assessment using best-practice measurement techniques and methods for evaluation of ongoing (i.e., post-registration) clinical competencies in the profession of pharmacy in Ontario, Canada. This model involves categorization of the profession into an active patient-facing and non patient-facing register, implementation of a learning portfolio requirement to replace mandatory continuing education credit accumulation, and the use of standardized assessment techniques, such as a multiple-choice test of clinical knowledge and an objective structured clinical examination (OSCE) of clinical reasoning and interpersonal skills. Lessons learned from the development, implementation and retrospective analysis of almost two decades of data from this program can provide regulators in diverse professions and different jurisdictions with tools for standardized assessment of patient care competencies.
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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 | 0.045 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".