Whole person medical education - moving beyond learning objectives and curriculum dot points
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".