Pharmacy education and workforce: strategic recommendations based on expert consensus in Lebanon
Bibliographic record
Abstract
Pharmacy in Lebanon has been taught for years, and the profession has known the golden ages in previous years. However, with the recent graduation of hundreds of pharmacists, without prior workforce planning, the oversupply of non-specialized pharmacists caused a mismatch with the needs of the market. The context of severe socioeconomic and sanitary crises has further exacerbated the situation, with hundreds of pharmacists leaving the country. A group of pharmacy experts joined to suggest strategic solutions to face such challenges, suggesting a clear strategy for education and the workforce, overarched by educational and professional values and based on six main pillars: (1) implement a national competency framework (including the core and specialized competency frameworks) to be used as a basis for licensure (colloquium); (2) implement a national pharmacy program accreditation, encompassing standards related to competencies adoption and assessment, curricula, teaching methods, research and innovation, instructors' and preceptors' skills, and experiential training; (3) organize training for students and early-career pharmacists; (4) optimize continuing education and implement continuous professional development, fostering innovation and specialization among working pharmacists; (5) develop and implement a pharmacy workforce strategy based on pharmacy intelligence, job market, and academic capacities; (6) develop and implement a legal framework for the above-mentioned pillars in collaboration with ministries and parliamentary commissions. Under the auspices of the relevant authorities, mainly the Order of Pharmacists of Lebanon and the Ministry of Education and Higher Education, the suggested strategy should be discussed and implemented for a better future for the pharmacy profession.
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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 | 0.043 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".