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Record W4406181535 · doi:10.34172/rdme.33262

Optimizing interprofessional education: Integrating a portfolio-based approach in undergraduate curriculum

2024· article· en· W4406181535 on OpenAlexaff
Sangeetha Kandasamy, Shivkumar Gopalakrishnan, Harikrishnan Elangovan, John Gilbert

Bibliographic record

VenueResearch and Development in Medical Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of British Columbia
FundersFoundation for Advancement of International Medical Education and Research
KeywordsPortfolioCurriculumInterprofessional educationMedical educationUndergraduate educationMedicineComputer scienceEngineering managementPsychologyMathematics educationEngineeringBusinessPedagogyHealth carePolitical science

Abstract

fetched live from OpenAlex

Background: Interprofessional education (IPE) is the need of the hour for any undergraduate curriculum to build interprofessional communication skills, adapt teamwork, and role clarity of various healthcare professionals in the early stage of a learning period. The outcome will improve patient safety and quality of care through a holistic approach by addressing lacunae in interprofessional collaboration. Objectives: To describe the steps to implement the IPE curriculum and evaluation of learners’ activity through an IPE portfolio. Results: Sensitization of IPE curriculum among various health professional faculties, and students. Develop core team facilitators, and construct lesson plans and assessments. The IPE portfolio will be used as a reliable and effective tool for formative assessment including authentic, real-world examples of learner’s work. Conclusion: This paper communicates undergraduates’ reflection practices in patient care stimulate critical thinking, deep and lifelong learning, and, facilitating from novice to mastery levels are achieved through integrated and aligned with IPE curriculum and portfolios-based assessment tool.

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)0.004
metaresearch head score (Gemma)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.054
GPT teacher head0.504
Teacher spread0.450 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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