Navigating complexity in team‐based clinical settings
Bibliographic record
Abstract
CONTEXT: Educators must prepare learners to navigate the complexities of clinical care. Training programmes have, however, traditionally prioritised teaching around the biomedical and the technical, not the socio-relational or systems issues that create complexity. If we are to transform medical education to meet the demands of 21st century practice, we need to understand how clinicians perceive and respond to complex situations. METHODS: Constructivist grounded theory informed data collection and analysis; during semi-structured interviews, we used rich pictures to elicit team members' perspectives about clinical complexity in neurology and in the intensive care unit. We identified themes through constant comparative analysis. RESULTS: Routine care became complex when the prognosis was unknown, when treatment was either non-existent or had been exhausted or when being patient and family centred challenged a system's capabilities, or participants' training or professional scope of practice. When faced with complexity, participants reported that care shifted from relying on medical expertise to engaging in advocacy. Some physician participants, however, either did not recognise their care as advocacy or perceived it as outside their scope of practice. In turn, advocacy was often delegated to others. CONCLUSIONS: Our research illuminates how expert clinicians manoeuvre moments of complexity; specifically, navigating complexity may rely on mastering health advocacy. Our results suggest that advocacy is often negotiated or collectively enacted in team settings, often with input from patients and families. In order to prepare learners to navigate complexity, we suggest that programmes situate advocacy training in complex clinical encounters, encourage reflection and engage non-physician team members in advocacy training.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".