Addressing the challenges of field notes in medical education: a qualitative study of resident experiences
Bibliographic record
Abstract
BACKGROUND: Evaluating resident physicians' competencies is critical in medical education to ensure high standards of patient care and professional development. Field notes are increasingly used as reflective tools in postgraduate medical education. Despite their growing use, skepticism about their effectiveness persists. This study aims to identify challenges with learner engagement in field notes and gather suggestions for operational improvements. METHODS: A qualitative study was conducted in the Department of Family & Community Medicine at the University of Toronto. Semi-structured interviews were conducted with seven postgraduate year one and year two family medicine residents. The interviews focused on residents' experiences and challenges with field notes. Data were analyzed using inductive thematic analysis to develop a comprehensive codebook, in alignment with Braun and Clarke's framework. RESULTS: Several key challenges with the use of field notes were identified including the redundancy of feedback, sporadic utilization, and time constraints for preceptors. Residents also expressed uncertainty about the expectations for using the tool and identified it as complex and cumbersome. Operational suggestions for improvement included the development of a mobile-friendly platform, streamlined functionality, a standardized and integrated feedback system, and clearer guidelines for use. CONCLUSIONS: The study highlighted significant challenges in the use of field notes within family medicine training programs and underscored the need for technological and procedural innovations to improve their effectiveness. Addressing these challenges through user-friendly design, clear guidelines, and integrated support systems could transform field notes into a more robust tool for competency-based medical education, benefiting residents, preceptors, and the broader medical community.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".