Progressive collaborative refinement on teams: implications for communication practices
Bibliographic record
Abstract
OBJECTIVES: Medical teaching teams (MTTs) must balance teaching and patient care in the face of three challenges: shifting team membership, varying levels of learners and patient complexity. To support care, MTTs rely on a combination of recurrent oral and written communication practices (genres), such as admission, progress and discharge notes. The purpose of this study was to explore how these genres influence the team's ability to collectively care for patients. METHODS: This was a multiple case study with data collected through observations and audio-recordings of 19 patient cases focusing on admission review discussions and chart documents throughout the hospitalisation. Participants included 14 medical students, 32 residents and 10 attending physicians rotating through one of three internal medicine MTTs. We used constant comparative analysis to identify recurrent patterns across the multiple cases, which were further elaborated in a return-of-findings focus group. RESULTS: The MTT genre system facilitated the care of patients through 'progressive collaborative refinement' (PCR): MTTs use case and data reviews to collaboratively and progressively refine their understanding of the patient's problems and develop strategies for addressing them. Progressive collaborative refinement was apparent through modifications made in the documentation. Although modifications were a necessary component, they were not sufficient: some modifications were made without refinement. We characterised incidents of failed modification as 'fragmentation'. Three types were observed: conceptualisation, documentation and continuity of care providers. In most cases, all three were present and interacted to impede PCR. CONCLUSIONS: Progressive collaborative refinement was used by MTTs to provide the optimal care to patients. Progressive collaborative refinement was impeded by a lack of continuity of care providers and gaps between communication genres that fragmented conceptualisation and documentation. Progressive collaborative refinement can be understood as both an overarching process and a shared but unstated ideal. Through defining and describing PCR, the present findings can be used to improve communication and teaching.
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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 | 0.036 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".