Cyclical Variation of Field Notes Completion in a Family Medicine Residency Program
Bibliographic record
Abstract
<ns3:p>Background Field Notes (FN) is a competency-based assessment tool used in Canadian Family Medicine programs, that provide low-stakes feedback to learners while documenting their progress towards competency. Data from the University of Toronto’s Department of Family and Community Medicine revealed cyclical variations in FN use over 10 years. We explored the perspectives of Family Medicine residents and preceptors regarding factors influencing motivators and barriers in FN generation. Methods Nine one-on-one interviews of residents and preceptors were conducted, recorded, de-identified, and transcribed. Thematic analysis of codes was performed using deductive and inductive techniques by three independent reviewers, with group review to resolve discrepancies. Results Five themes emerged: 1) FN completion correlated with residents’ learning curves, 2) curricular factors impacted FN use, 3) FN were influenced by vacation periods, 4) Residents and preceptors had different motivators for FN initiation, and 5) barriers such as administrative burdens and lack of standardized guidelines hindered FN use. Conclusions Strategic interventions such as scheduled reminders, standardized guidelines, and improvements to the FN platform could enhance FN utilization, ensuring consistent feedback and promoting competency development. Future studies should involve larger samples across multiple institutions to validate these findings and enhance generalizability across Canadian Family Medicine programs.</ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".