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Record W2755952716

Implementation and evaluation of a marketing for pharmacists elective course

2017· article· en· W2755952716 on OpenAlexaff
Jason Perepelkin

Bibliographic record

VenuePharmacy Education · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCourse (navigation)PharmacyMedical educationPlan (archaeology)Course evaluationPharmacy practicePsychologyMedicineNursingHigher educationEngineeringPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This manuscript evaluates the introduction and evaluation of a marketing for pharmacists course, an elective course offered to student pharmacists in the last year of the entry-to-practice program. A questionnaire, consisting of 24 items, centring on methods of delivery, course content, and outcomes was distributed to, and completed by, students on the final day of the course. There was a strong sense of satisfaction with taking the elective course, with most stating he/she would recommend taking the course to future students. More support was given to keeping the course heavily focused on a project-based assessment of knowledge and learning, as opposed to reducing the project-based assessment weighting and including a final examination. Overall there is robust support from those that have taken the course, and most find they will have a unique advantage in his/her career because of taking the course. There is also the added benefit of engaging pharmacy stakeholders, and in particular practitioners that work directly with student groups in creating a marketing plan.

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)0.025
metaresearch head score (Gemma)0.032
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 score0.025
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.092
GPT teacher head0.568
Teacher spread0.476 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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