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Record W4283713128 · doi:10.1016/j.jsps.2022.06.023

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

2022· article· en· W4283713128 on OpenAlexfundno aff
Taher Hatahet, Hala Al-Obaidi, Ismaiel A. Tekko, Tianbao Chen

Bibliographic record

VenueSaudi Pharmaceutical Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsCurriculumAccreditationPharmacyBachelorCurriculum developmentExperiential learningPopulationMedical educationPolitical sciencePublic relationsMedicinePedagogySociologyNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.118
GPT teacher head0.424
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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