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Record W3047580003 · doi:10.1186/s12909-020-02109-1

Evaluating prescribing competencies covered in a Canadian-accredited undergraduate pharmacy program in Qatar: a curriculum mapping process

2020· article· en· W3047580003 on OpenAlexaboutno aff
Oraib Abdallah, Rwedah Anwar Ageeb, Wishah Hamza Imam Elkhalifa, Monica Zolezzi, Alla El‐Awaisi, Mohammad Diab, Ahmed Awaisu

Bibliographic record

VenueBMC Medical Education · 2020
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
FundersQatar University
KeywordsCurriculumPharmacyAccreditationMedical educationMedicineBachelorPharmacistNursingPsychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this study was to evaluate the existing Bachelor of Science in Pharmacy [BSc (Pharm)] curriculum at Qatar University College of Pharmacy (QU CPH), for addressing international prescribing competencies. METHODS: The Australian National Prescribing Service (NPS MedicineWise) Competencies Required to Prescribe Medicines framework (the Prescribing Competencies Framework) was used in the BSc (Pharm) curriculum mapping process. The NPS MedicineWise Prescribing Competencies Framework outlines seven competency areas that are essential for pharmacist prescribing. The first mapping activity assessed the learning outcomes (LOs) of 62 courses within the BSc (Pharm) curriculum for covering and addressing the NPS MedicineWise competencies. The second mapping activity involved matching the LOs identified to address the NPS MedicineWise prescribing competencies, to the 2017 Association of Faculties of Pharmacy of Canada (AFPC) educational outcomes, on which the QU CPH BSc (Pharm) program is based. The AFPC educational outcomes address seven key program-level learning outcomes. RESULTS: The QU CPH BSc (Pharm) curriculum addresses most of the prescribing competencies listed in the NPS MedicineWise Prescribing Competencies Framework. However, gaps were identified in the curricular content and in the LOs that were related, but not restricted, to the following: electronic prescribing, physical examinations/preparing patients for investigations, and policies/procedures and quality assurace related to prescribing. Other gaps identified include legislative and workplace requirements for obtaining consent to access confidential patient's health information. CONCLUSION: The curriculum mapping exercise provided evidence that, for the most part, the existing BSc (Pharm) curriculum at QU CPH prepares pharmacy graduates for prescribing. However, there are areas that need better alignment between the taught curriculum and training on prescribing in practice. The results of this study are important to consider if pharmacist prescribing is to be implemented in Qatar.

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)0.039
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.248
GPT teacher head0.495
Teacher spread0.246 · 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 designQualitative
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

Citations7
Published2020
Admission routes1
Has abstractyes

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