Development of a Prior Learning Assessment for Pharmacists Seeking Licensure in Canada
Bibliographic record
Abstract
Prior learning assessment (PLA) has been used to provide an indication of learning acquired through formal educational and unstructured professional experiences. PLA has been used in a variety of professions and trades to complement traditional credential-based evaluations of knowledge and skills. Within the context of pharmacy, PLA is currently being used as a tool to assess the competencies of foreign- trained pharmacists seeking licensure in Ontario, Canada. A competency-based approach moves beyond the traditional prior learning tools (e.g. interviews, portfolios, and transcript reviews) and incorporates performance-based assessment such as the objective structured clinical examination (OSCE). This paper describes the systematic method for developing a structured, competency-based prior learning assessment for foreign-trained pharmacists seeking licensure in Ontario, Canada. Beginning with the identification of critical competency standards, a model for sequen- tial assessment of knowledge, skills and values is presented. Results from a pilot program are presented, suggesting the importance of cultural competency (over and above linguistic competency and in conjunction with a strong declarative pharmacotherapeutic knowledge base) in pharmacy practice.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".