Towards a national pharmaceutical strategy in Lebanon: Ensuring access to quality and safe medications for all
Bibliographic record
Abstract
Introduction: Lebanon is facing challenges affecting the whole health sector, including access to medications. Lebanon has only proposed very few short-term national pharmaceutical strategic solutions. Previous reform attempts targeting the pharmaceutical sector, could not protect it from the crises and their detrimental consequences on patient and population health. Purpose: This document unveils the critical elements that should be addressed in the planned National Pharmaceutical Sector Strategy (NPS) being developed by the Order of Pharmacists of Lebanon (OPL) in consultation with the concerned stakeholders. Method: Strategic goals were proposed for adoption and implementation by the competent authorities based on consultations, situational assessments, and gap analyses. The objectives and an implementation plan were developed based on the available resources and policy dialogue, respectively. Conclusion: The National Pharmaceutical Strategy would help the Lebanese authorities/policy-makers, aided by competent healthcare professionals, develop and implement a time-bound roadmap to attain a nation with access to quality and safe medications for the whole population. Implementing this strategy would require the commitment of decision-makers, the accountability of involved parties, innovation in finding solutions, close collaboration between stakeholders, and lengthy efforts to attain the stated vision.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".