Navigating Challenging Conversations: The Interplay Between Inquiry and Knowledge Drives Preparation for Future Learning
Bibliographic record
Abstract
Introduction: they use the workplace context to build their capabilities. Because physicians rarely pursue formal professional development activities to improve communication skills, examining physician-patient communication offers a powerful opportunity to illuminate important aspects of preparation for future learning in the workplace. Methods: This qualitative observational study involved over 100 hours of observation of eight pediatric rehabilitation physicians as they interacted with patients and families at an academic teaching hospital in 2018-2020. Detailed field notes of observations, post-observation interviews, and exit interviews were the data sources. Data collection and analysis using a constructivist grounded theory approach occurred iteratively, and themes were identified through constant comparative analysis. Results: Through their daily work, experienced physicians employ 'habits of inquiry' by constantly seeking a better understanding of how to navigate challenging conversations in practice through monitoring and attuning to situational and contextual cues, taking risks and navigating uncertainty while exploring new and varied ways of practicing, and seeking why their strategies are successful or not. Discussion: Engaging in communication challenges drives physician learning through an interplay between habits of inquiry and knowledge: inquiry into how to improve their communication supported by existing conceptual knowledge to generate new strategies. These 'habits of inquiry' prompt continual reinvestment in problem solving to refine existing knowledge and to build new skills for navigating communication challenges in practice.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".