Prior Learning Assessments in a Professional Workplace for Practising Pharmacists and Technicians
Bibliographic record
Abstract
Background: A prior learning assessment (PLA) is a summative statement of an individual's learning acquired through education and experience. We developed PLA surveys for 3 groups of pharmacy staff: experienced pharmacists with supervisory or clinical roles; pharmacists entering a pharmacy practice residency program; and experienced pharmacy technicians. Methods: Each PLA survey was developed based on a literature review and desirable learning outcomes for a regional pharmacy program. PLAs consisted of numerous potential learning needs, including possible job roles, competencies, essential skills and areas of practice expertise in 11 domains. Pharmacy staff scored past exposure, perceived ability (prior experience) and interest for each potential learning need. Learning needs were calculated as interest score minus ability score. Results: 23 of 38 (61%) experienced pharmacists, all 24 (100%) pharmacy residents and all 17 (100%) pharmacy technicians invited to complete the PLA responded. For each of the 11 domains, Cronbachs alpha scores were greater than 0.69. For experienced pharmacists, the highest learning needs occurred in technical domains (drug distribution and computer/informatics), with low needs in practice management and patient care. For pharmacy residents, the highest learning needs occurred in patient care domains. Pharmacy technician learning needs were greatest in human resources and drug distribution. Conclusion: We developed PLA surveys for experienced pharmacists, pharmacy residents and pharmacy technicians that demonstrate internal consistency reliability. Regulatory bodies, education providers, employers, managers and individual pharmacy personnel can use PLA to identify learning needs either prior to a practice change or as part of continuing professional development planning.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".