It's not just what you know: junior trainees' approach to follow‐up and documentation
Bibliographic record
Abstract
CONTEXT: In teaching hospitals, junior trainees (first-year residents and third-year medical students) are responsible for patient follow-up and documentation under the supervision of senior team members. In order to support trainees in their role, supervisors need to understand how trainees approach these tasks and how they can be coached to develop best practices. OBJECTIVES: The purpose of our study was to explore the range of practices used by junior trainees in clinical settings. METHODS: Constructivist grounded theory was used to guide the collection and analysis of data on follow-up and documentation during 34 observation periods with 17 junior trainees. Data sources included field notes, field interviews and de-identified copies of patient charts. We also held two focus groups with four attending physicians in each. RESULTS: We were able to describe three interrelated characteristics that influenced a trainee's approach to and ability to perform the tasks of patient follow-up and documentation: (i) diligence; (ii) relationship to the team (dependent, independent, collaborative), and (iii) level of performance (Data Gatherer, Sensemaker, Manager). Diligence and relationship to the team appeared to influence the quality and focus of a trainee's approach at all levels of performance. Level of performance was felt, by focus group attending physicians, to reflect a developmental progression of knowledge and skills. CONCLUSIONS: Our findings contribute to the existing literature in three ways. Firstly, they extend our understanding of how junior trainees approach the task of in-patient follow-up and clinical documentation and the value of those activities. Secondly, they provide new insights to support formative and summative assessment. Finally, they contribute to a growing body of literature exploring the factors that impact trainees' roles and interactions with the team. Future research should focus on validating our findings and exploring their utility in the development of novel assessment strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".