Academic pharmacist competencies in ordinary and emergency situations: content validation and pilot description in Lebanese academia
Bibliographic record
Abstract
BACKGROUND: In the absence of a similar study in the Lebanese context, this study aimed to validate the content of the specialized competencies frameworks of academic pharmacists (educators, researchers, and clinical preceptors) and pilot their use for practice assessment in the context of multiple severe crises. METHODS: A web-based cross-sectional study was conducted between March and September 2022 among academic pharmacists enrolled by snowball sampling using a questionnaire created on Google Forms. RESULTS: The suggested frameworks had appropriate content to assess the competencies of academic pharmacists. Educators and clinical preceptors were confident in all their competencies except for emergency preparedness. Researchers had varying levels of confidence, ranging from moderate to high confidence for many competencies, but gaps were reported in fundamental research, conducting clinical trials, and pharmacy practice research (mean < 80). Educators and researchers relied primarily on experience and postgraduate studies, while clinical preceptors emphasized undergraduate studies to acquire their respective competencies. Continuing education sessions/programs were the least cited as a competency-acquiring venue across all roles. CONCLUSION: This study could develop and validate the content of frameworks for specialized competencies of academic pharmacists, including educators, researchers, and clinical preceptors, in a challenging setting. The frameworks were also piloted for practice assessment, which could contribute to supporting effective performance and sustained development of practitioners and help link the skills and competencies pharmacists learn during their studies with those required for a career in academia.
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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 | 0.018 | 0.021 |
| 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 | 0.001 |
| 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 | 0.000 |
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".