Validation of the specialized competency framework for pharmacists in hospital settings (SCF–PHS): a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to validate the content of the specialized competency frameworks for pharmacists working in hospital settings (hospital and clinical pharmacists) and pilot the frameworks for practice assessment. METHODS: This online cross-sectional study was carried out between March and October 2022 among a sample of 96 Lebanese pharmacists working in hospital settings. The frameworks were distributed to full-time hospital and clinical pharmacists, who filled them out according to their role in the hospital. RESULTS: Overall, the competencies were distributed over five domains for hospital pharmacists (fundamental skills, safe and rational use of medicines, patient-centered care, professional skills, and preparedness for emergencies), while for clinical pharmacists, competencies were distributed over seven domains (quality improvement, clinical knowledge and skills, soft skills, ability to conduct clinical research, ability to provide effective education, use information technology to make decisions and reduce errors, and emergency preparedness). Moreover, Cronbach alpha values were appropriate, indicating sufficient to high internal consistency. Pharmacists were highly confident in most competencies, with some exceptions related to research in emergency settings (data evaluation, research, and reporting). CONCLUSIONS: This study could validate competency frameworks for clinical and hospital pharmacists, with the competencies and their respective behaviors showing an adequate construct analysis. It also identified the domains that require further development, i.e., soft skills and research in emergency settings. Both these domains are timely and needed to overcome the current practice challenges in Lebanon.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".