Strategic job market-guided development of pharmacy bachelor‘s curriculum and its importance in maintaining the profession viability in the Middle Eastern countries: Colleges of pharmacy in the UAE as a model
Bibliographic record
Abstract
The Middle East has high youth population; however, it is challenged by uncertain economic situation. Higher education plays a crucial role in the development of nations by equipping generations with the knowledge and skill through cumulative curriculum development. Like other professions, pharmacy is a dynamic field of study where continuous improvements are required to keep the viability of the profession and endow future generations with up to date skills. This article describes a strategy for pharmacy curriculum development considering four layers. The strategy starts from the understanding of the current situation in a university, looking into national, international accreditations and job market. The strategy covers development from program to subject's level. The strategy is applied to pharmacy programs in the UAE. Upon analysis, several recommendations were obtained for curriculum improvements. At individual university level, there is a need to work on clinical oriented topics in the curriculum to fit with international accreditation and country's vision. Details on this can be taken form deeper analysis of job market and stakeholders in the UAE. On the national level, unifications of total credit hours for the degree across universities needs to be envisaged with limits on contact experiential hours. The strategy has the potential of extrapolating to other Middle Eastern countries.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".