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Record W2806229007 · doi:10.1080/19415257.2018.1474490

A pilot study on interprofessional education: how prepared are faculty to teach?

2018· article· en· W2806229007 on OpenAlexaff
Tracy Christianson, Lesley Bainbridge, Colleen Halupa

Bibliographic record

VenueProfessional Development in Education · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of British ColumbiaThompson Rivers University
Fundersnot available
KeywordsPreparednessFaculty developmentMedical educationInterprofessional educationProfessional developmentPsychologyTeaching methodQualitative researchMedicinePedagogyHealth careSociology

Abstract

fetched live from OpenAlex

Faculty development for interprofessional (IP) teaching and learning is a complex and evolving part of educators’ preparation for IP teaching and learning. A review of the literature highlighted a gap of rigorous research in the area of faculty development for interprofessional education (IPE). This pilot study used a mixed-methods approach to explore how faculty development affected educators’ preparedness for IP teaching and looked at the possible effects of IP and teaching experiences. Pre- and post-faculty development evaluations were captured using validated instruments and helped to explore the impact faculty development had on educators’ preparedness for IPE. The qualitative data offered insights using participants’ perspectives about IPE where the quantitative method could not. This pilot study offers findings that explored important characteristics that may have a role in faculty preparation for IPE teach and learning and could possibly be used in future research.

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)0.011
metaresearch head score (Gemma)0.023
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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0010.002
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 head0.071
GPT teacher head0.479
Teacher spread0.408 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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