Documentation as composing: how medical students and residents use writing to think and learn
Bibliographic record
Abstract
Some educators have described clinical documentation as "scut". Research in medicine has focused on documentation's communicative value and not its function in learning. With time being an important commodity and electronic health records changing how we document, understanding the learning value of documentation is essential. The purpose of this study was to explore how trainee composing practices shape learning. Qualitative methods employing Rhetorical Genre Theory were used to explore clinical documentation practices among medical trainees. Data collection and analysis occurred in iterative cycles. Data included field notes and field interviews from 110 h of observing junior trainees and senior internal medicine residents participating in patient admission and follow-up visits. Analysis was focused on Paré and Smart's framework for studying documentation as composing. From a composing lens, documentation plays a vital role in learning in clinical settings. Junior trainees were observed to be reliant on using writing to support their thinking around patient care. Before patient encounters, writing helped trainees focus on what was already known and develop a preliminary understanding of the patient's problem(s). After encounters, writing helped trainees synthesize the data and develop an assessment and plan. Before and after the encounter, through writing, trainees also identified knowledge and data collection gaps. Our findings highlight clinical documentation as more than a communication task. Rather, the writing process itself appeared to play a pivotal role in supporting thinking. While some have proposed strategies for reducing trainee involvement, we argue that writing can be time well spent.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".