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Record W2346122348 · doi:10.3109/13561820.2016.1141752

Interprofessional education and practice guide No. 5: Interprofessional teaching for prequalification students in clinical settings

2016· article· en· W2346122348 on OpenAlexaffabout
Désirée Lie, Christopher P. Forest, Lynn Kysh, Lynne Sinclair

Bibliographic record

VenueJournal of Interprofessional Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
FundersHealth Resources and Services Administration
KeywordsPreceptInterprofessional educationAccreditationMedical educationHealth professionsMedicineCurriculumInstitutionNursingHealth carePsychologyPedagogySociology

Abstract

fetched live from OpenAlex

The importance of interprofessional education in health professions training is increasingly recognised through new accreditation guidelines. Clinician teachers from different professions may find themselves being asked to teach or supervise learners from multiple health professions, focusing on interprofessional dynamics, interprofessional communication, role understanding, and the values and ethics of collaboration. Clinician teachers often feel prepared to teach learners from their own profession but may feel ill prepared to teach learners from other professions. In this guide, we draw upon the collective experience from two countries: an institution from the United States with experience in guiding faculty to teach in a student-run interprofessional clinic and an institution from Canada that offers interprofessional experiences to students in community and hospital settings. This guide offers teaching advice to clinician educators in all health professions who plan to or already teach in an interprofessional clinical setting. We anticipate that clinician teachers can learn to fully engage learners from different professions, precept effectively, recognise common pitfalls, increase their confidence, reflect, and become role models to deliver effective teaching in interprofessional settings.

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)0.008
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.566
Teacher spread0.526 · 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 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

Citations50
Published2016
Admission routes2
Has abstractyes

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