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Record W4283656218 · doi:10.3390/pharmacy10040071

Ranking of Curricular Content by Pharmacy Students and Community Pharmacists

2022· article· en· W4283656218 on OpenAlexaffabout
Jeffrey G. Taylor, Holly Mansell, Jason Perepelkin, Danielle Larocque

Bibliographic record

VenuePharmacy · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPharmacyGraduation (instrument)Medical educationPerspective (graphical)Ranking (information retrieval)PsychologyMedicinePharmacistFamily medicine

Abstract

fetched live from OpenAlex

A cross-sectional survey was conducted to determine the relative rankings of 17 key components in an undergraduate program. The degree of discrepancy between curricular content and that of student and pharmacist expectations was also of interest. An online questionnaire was emailed to both groups in one Canadian province. Respondents considered four questions related to the nature and adequacy of education they did receive (pharmacists) or should receive (students) and the relative importance of key topic areas (along 11-point scales). The response rate was 31.0 (students) and 10.8 percent (pharmacists). As expected, both students and pharmacists identified therapeutics and patient counselling as critical focal points for the program, while the importance of compounding was mixed. Most topics were deemed as best handled during the didactic program, with students seeing greater value in learning a larger portion of two skills (injection training and managerial duties) post-graduation. In conclusion, discrepancies were indeed found. For students, topics such as injection training and minor ailment prescribing were perceived as receiving too little attention, while communication, pharmaceutical sciences, and professionalism received too much attention. In a significant departure in perspective, pharmacists rated communication, ethical decision-making, and professionalism almost two points higher than did students.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.113
GPT teacher head0.447
Teacher spread0.334 · 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 teacher head, not a consensus.

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

Citations7
Published2022
Admission routes2
Has abstractyes

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