MétaCan
Menu
Back to cohort
Record W2785219074 · doi:10.26443/ijwpc.v1i1.132

Whole person medical education - moving beyond learning objectives and curriculum dot points

2018· article· en· W2785219074 on OpenAlexvenueno aff
Hilton Koppe

Bibliographic record

VenueInternational Journal of Whole Person Care · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsPaceSyllabusCurriculumMedical educationExperiential learningPsychologyHealth careMedicinePedagogy

Abstract

fetched live from OpenAlex

In recent years, great progress has been made in teaching medical students, residents and physicians to be more patient focused in their clinical interactions. Patient-centred medical care initially, and now whole person care, have helped to improve patient experiences of the health system, and led to better patient outcomes.Unfortunately, during this time, teaching methods have not moved forward at the same pace. Medical education is often teacher focussed and syllabus driven. Emphasis is put on how to pass assessment tasks and exams. The learner as a person can be as invisible as the patient once was before the changes towards more person-centred medical care.It is time for a paradigm shift in medical education - for whole person care principles to be applied in education as they have been in clinical care.This experiential, interactive workshop will challenge participants to think about how they deliver educational material for students, residents and continued professional development. Whole person care principles will be applied to both the planning of an educational event, and to strategies and techniques for delivery of education. Participants will have an opportunity to practice and refine these techniques.Participants can expect to leave the workshop equipped, inspired and ready for the challenge of leading an education paradigm change within their home institution. Or maybe they might just have a bit of fun with like-minded colleagues, thinking about new ways of teaching.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.330
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Whole Person CareSame topicInnovations in Medical EducationFrench-language works237,207