The development of a novel medical education elective for fourth-year medical students
Bibliographic record
Abstract
Postgraduate medical trainees (residents) have a wellestablished role in the teaching and training of undergraduate medical students across specialties.1 The need for these postgraduate trainees to be supported in their roles as key facilitators of undergraduate medical education has led to the mandatory inclusion of residents-as-teachers programs in all postgraduate residency training programs across Canada.2 In the United Kingdom, the National Health Service organizations are responsible for including a contractual requirement for doctors to carry out teaching.3 In the United States, the Accreditation Council for Graduate Medical Education (ACGME) Common Program Requirements for practice-based learning and improvement has outlined that residents/fellows are expected to develop skills and habits to be able to participate in the education of patients, families, students, residents and other health professionals, as documented by evaluations of a resident’s teaching abilities by faculty and/or learners.4 However, due to concurrent service requirements during residency, it is difficult for residents to devote time to enhancing teaching skills beyond the mandatory resident-as-teacher training program. Moreover, there is variability in the duration of such programs, with some devoting only a few hours to developing teaching skills.5,6 To enhance teaching confidence and abilities prior to residency, a number of institutions have developed programs for senior medical students to participate as mentors and near-peer instructors.7 Additionally, a number of medical schools in the United Kingdom and the United States have developed a formal curriculum for medical students to become junior clinical teachers.8-12 The transition from learner to teacher can conceivably be facilitated in the last year of medical school training. One prior study supports the use of this time to shape the professional identify of medical trainees.13 Without competing service requirements, an elective focused on teaching would be ideally placed in the fourth or final year of medical school training. In January 2015, the University of Ottawa introduced a clinical teaching elective (CTE) to fourth year medical students titled An Introduction to the Art and Science of Clinical Teaching. The purpose of this paper is to outline the development of this elective and its core curricular components.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".