Exploring How Pediatric Residents Develop Adaptive Expertise in Communication: The Importance of “Shifts” in Understanding Patient and Family Perspectives
Bibliographic record
Abstract
PURPOSE: Communication with patients and families can be complex, especially in challenging discussions. To communicate effectively, expert physicians must often use flexible approaches. This innovative use of knowledge to handle complexity is an essential capability of adaptive expertise. Despite its importance for effective communication and implications for medical education, little is known about how adaptive expertise develops in trainees. The purpose of this study was to explore how pediatric residents developed adaptive expertise in communication. METHOD: A constructivist grounded theory study, using observations of physician-patient communication and semistructured interviews as data sources and purposeful sampling of 10 pediatric subspecialty residents at the University of Toronto, was conducted in 2016-2017. Data collection and analysis occurred iteratively, and themes were identified through the research team's constant comparative analysis. RESULTS: Residents navigated challenging discussions with patients and families by enabling them to express their own narratives and integrating these with their medical knowledge to provide care. At times, a "shift" in the residents' understanding of the families' perspectives was needed to effectively navigate the discussion. Residents used this shift purposefully to create new communication strategies, resulting in an opportunity for learning. CONCLUSIONS: "Shifts" are defined as adjustments in the resident's understanding of a family's perspective that affect clinical care. Analysis suggests that these "shifts" can be understood to support development of adaptive expertise. The workplace learning environment promoted this development by providing opportunities that prepared residents for future learning through active experimentation, offering multiple perspectives and enhancing deeper conceptual learning.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".