Reflecting on professional identity in undergraduate medical education: implementation of a novel longitudinal course
Bibliographic record
Abstract
BACKGROUND: Today's healthcare professionals face numerous challenges. Improving reflection skills has the potential to contribute to the better management of complex patients and healthcare systems, as well as to improve professional practice. However, the question of how reflection skills can inform professional identity development at the undergraduate medical education level remains unanswered. APPROACH: The authors developed and implemented a 4-year course that aims to engage students in a reflective process to increase their awareness of their professional identity development. The course is structured around three types of pedagogical activities: workshops, reflections deposited in an electronic portfolio, and individual discussions with mentors. EVALUATION: Sixty-four 1st year students (33%) and 17 mentors (50%) from the 2017-2018 cohort completed evaluation questionnaires. For the 2018-2019 academic year, 73 1st year students (34%) and 27 2nd year students (14%), as well as 20 1st year (59%) and 19 2nd year mentors (57%) replied. Students and mentors considered that the pedagogical activities contributed to the development of students' professional identity through the acquisition of reflection skills, but some elements were perceived as challenging, notably, completing the portfolio, finding a subject to reflect about and the timing of the proposed activities. REFLECTION: An important preoccupation when wanting to foster the development of professional identity through the acquisition of reflection skills is the authenticity of students' reflection. We tried to favor authentic reflection, by having a mentee-mentor pair throughout the entire 4‑year course. A rigorous evaluation process helped us identify and promptly correct issues as they surfaced.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".