Using field notes to evaluate competencies in family medicine training: a study of predictors of intention
Bibliographic record
Abstract
BACKGROUND: Documenting feedback during clinical supervision using field notes (FN) is a recommended competency-based evaluation strategy that will require changes in the culture of medical education. This study identified factors influencing the intention to adopt FN in family medicine training, using the theory of planned behaviour. METHODS: This mixed-methods study involved clinical teachers (CT) and residents from two family medicine units. Main outcomes were: 1) intention (and its predictors: attitude, perceived behavioural control (PBC) and normative belief) to use FN, assessed using a 7-item Likert scale questionnaire (1: strongly disagree to 7: strongly agree) and 2) related salient beliefs, explored in focus groups three and six months after FN implementation. RESULTS: 27 CT and 28 residents participated. Intention to use FN was 6.20±1.20 and 5.74±1.03 in CT and residents respectively. Predictors of this intention were attitude and PBC (mutually influential: p < 0.05), and normative belief (p < 0.01). Focus groups identified underlying beliefs regarding their use (perceived advantages/disadvantages and facilitators/barriers). CONCLUSION: Intention to adopt field notes to document competency is influenced by attitude, perceived behavioural control and normative belief. Implementation of field notes should be preceded by interventions that target the identified salient beliefs to improve this competency-based evaluation strategy.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".