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Record W4205888680 · doi:10.3390/pharmacy10010012

iEthics: An Interprofessional Ethics Curriculum

2022· article· en· W4205888680 on OpenAlexaff
Victoria Wood, Lynda Eccott, P. Crowell

Bibliographic record

VenuePharmacy · 2022
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCurriculumCurriculum developmentInterprofessional educationMedical educationEngineering ethicsCurriculum mappingPsychologyMedicinePedagogyHealth carePolitical scienceEngineering

Abstract

fetched live from OpenAlex

This article discusses the development, content, implementation, and evaluation of an interprofessional ethics curriculum that has been integrated as a required component of learning in the Faculty of Pharmaceutical Sciences at the University of British Columbia (UBC), along with 12 other health professional programs. We start by giving a background and rationale for the development of the integrated ethics (iEthics) curriculum, led by UBC Health, and provide an overview of the pedagogical approach used, curriculum model, and content. We outline the way in which the iEthics curriculum has been implemented in the Faculty and share findings from program evaluations. In the discussion section, we reflect on our experience as facilitators for the interprofessional workshops and link these experiences with the findings from the program evaluations. These reflections highlight the way in which the iEthics curriculum has been successful in meeting the desired outcomes of learning in terms of the interprofessional delivery, and provide insights into how the findings from the iEthics evaluation informed other modules in the integrated curriculum and its implementation in the Faculty of Pharmaceutical Sciences.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0370.001

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 head0.126
GPT teacher head0.564
Teacher spread0.438 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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