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Record W2474505563

Administration of a Canadian critical appraisal exam to pharmacy students in the Middle East

2016· article· en· W2474505563 on OpenAlexaffabout
Emily Black, Kerry Wilbur, Wessam Elkassem, David M. Gardner

Bibliographic record

VenueQatar University QSpace (Qatar University) · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCritical appraisalPharmacyMedical educationCurriculumCritical thinkingPsychologyContext (archaeology)LicensureMedicinePerformance appraisalPedagogyNursingAlternative medicineManagement
DOInot available

Abstract

fetched live from OpenAlex

Introduction/Context: Critical appraisal training of pharmacy students is important in the development of knowledge and skills necessary for the application of evidence-based practice post-licensure. The objectives of this study were to evaluate critical appraisal skills of students in Qatar and to compare performance across applicable academic years. Description of Assessment: Pharmacy students in third year, fourth year, and Doctor of Pharmacy students at Qatar University completed an application-based critical appraisal exam developed by faculty from a Canadian University. Results were categorised according to Bloom's taxonomy and compared by academic year. Evaluation: The median score of students was 30.5%. Students performed best on questions categorised as comprehension and lowest on evaluation questions. A significant improvement in performance as students progressed through the curriculum was observed. Implementation and Future Plans: Findings will be used to refine the current critical appraisal course series to increase emphasis on application of critical appraisal skills. 2016 FIP.

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)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.231
GPT teacher head0.447
Teacher spread0.216 · 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.

Study designObservational
DomainMethods
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

Citations2
Published2016
Admission routes2
Has abstractyes

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