Is there a role for a learner education handover as part of the Medical Council of Canada assessment and licensing process?
Bibliographic record
Abstract
The transition from undergraduate medical education (UGME) to postgraduate medical education (PGME) is a time of vulnerability for medical schools, postgraduate residency programs, and most importantly, traineesThere is a disconnect between the UGME and PGME experience. Student information shared by UGME is primarily summative of knowledge and skills; PGME programs are unaware of specific learner accommodation requirements, tailored supervisory needs, or potential professionalism concerns identified during UGMEThis lack of integration between UGME and PGME increases potential risk to learners, postgrad programs and patientsBetter linkages and communication along the education continuum could optimize learning and reduce inefficiency and riskThe Medical Council of Canada (MCC) has asked if there is a role for a learner handover (LH) within their licensing processes; however the intended purpose of an LH must first be determinedA Canadian-based LH referred to as a Learner Education Handover (LEH) model including disclosure of student learning/disability accommodation needs, general health concerns, EDI/religious requirements, professionalism concerns, and recommendations for special focus in residency of specific areas of medical knowledge/skill is described.Findings from beta and pilot testing support the value and feasibility of the LEH model. Fundamental principles are outlined: LEH occurs post-residency matchLEH should be forward facing; focused on ongoing or recurring learner issues and needsLearners must be included in the processImplementation would require participation by all Canadian medical schools and all learnersImplementation challenges include: Ensuring learner safety following information disclosureEngaging UGME DeansProtection of information ensuring a 'need-to-know' status is maintainedIncorporating the LEH into the licensing activity could enable the MCC to support a system that proactively responds to learner needs, optimizes physician performance and promotes safe, high quality patient care.
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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".