The specialized competency framework for community pharmacists (SCF-CP) in Lebanon: validation and evaluation of the revised version
Bibliographic record
Abstract
BACKGROUND: In the absence of similar studies in Lebanon, this study aimed at upgrading and validating the Lebanese specialized competencies framework for community pharmacists (SCF-CP) as a tool to transform community practice and support the professional development and career progression of community pharmacists. METHODS: Content validity was assessed and improved through a team of experts. After a thorough literature review and utilizing the Delphi technique, six domains were defined in the framework, with their respective competencies and behaviors. A cross-sectional study was then carried out from March to October 2022 using an online questionnaire created on Google Forms. The snowball technique was applied to reach community pharmacists across all the Lebanese governorates. RESULTS: The final sample included 512 community pharmacists. The construct validity of the framework was confirmed by factor analysis. The Kaiser-Meyer-Olkin measures of sampling adequacy were satisfactory for all models ranging from 0.500 to 0.956 with a significant Bartlett's test of sphericity (P < 0.001). The internal consistency of all competency domains was confirmed by Cronbach's alpha, with values ranging from 0.803 to 0.953. All competencies were significantly correlated with their respective domains (P < 0.001), and all domains were significantly correlated with each other and with the framework (P < 0.001). The participants declared being competent in all domains relating to fundamental skills, safe and rational use of medicines, pharmacy management, professional skills, public health fundamentals, and emergency preparedness and response, with some exceptions, such as compounding, management, and emergency preparedness. A higher declared competency level was associated with having more experience and receiving more than 50 patients per day. CONCLUSION: Our findings could demonstrate that the Lebanese specialized competency framework is a valid and reliable tool. This framework could help assess the minimum competencies that community pharmacists should possess or acquire and direct initial and continuing education for better practice. Hence, it could be adopted by the authorities and implemented in the Lebanese community pharmacy setting.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".