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Record W2160463721

International Exposure to Pharmacy Leadership, Education and Practice: The Early Qatar Experience

2014· article· en· W2160463721 on OpenAlexfundno aff
Emily Black, Kyle John Wilby, Peter J. Jewesson

Bibliographic record

VenueQatar University QSpace (Qatar University) · 2014
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicMedical and Pharmaceutic Studies
Canadian institutionsnot available
FundersFaculty of Pharmaceutical Sciences, University of British ColumbiaQatar University
KeywordsInternshipPharmacyPharmacy practiceMedical educationMedicinePharmaconomistPolitical scienceNursing
DOInot available

Abstract

fetched live from OpenAlex

Introduction: The College of Pharmacy (CPH) at Qatar University (QU) offers international experiences for Doctor of Pharmacy (PharmD) students from North America. The objective of these rotations is to provide students with exposure to pharmacy practice and education in a progressive Arab country. Design: Each internship consisted of 5 core components: i) Qatar national priorities, strategic planning, and educational system; ii) academic leadership; iii) student instruction; iv) project, and; v) pharmacy practice. Evaluation: Since 2010, eleven students from three universities have successfully completed elective experiences. Students consistently rated rotations highly in terms of organisation, unique experiences, and exposure to faculty with diverse backgrounds. Additional benefits to the host and parent colleges included student exchange, programme development, research collaboration and recruitment. Future Plans: International experiences have been well received and will continue to be offered in order to broaden student perspective of pharmacy education and practice in the Middle East.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.161
GPT teacher head0.406
Teacher spread0.245 · 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 designObservational
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

Citations3
Published2014
Admission routes1
Has abstractyes

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