Unhurried Conversations in Health Care Are More Important Than Ever: Identifying Key Communication Practices for Careful and Kind Care
Bibliographic record
Abstract
Unhurried conversations are necessary for careful and kind care that is responsive and responsible to both patients and clinicians. Adequate conceptual development is an important first step in being able to assess and measure this important domain of quality of care. In this article, we expand on a preliminary model to identify the key microlevel communication practices that support an unhurried conversation, defined as an ongoing, mutual accomplishment between patient and clinician that proceeds through a range of verbal and nonverbal communication practices wherein one or more participants (mutually) regulate the sequence, spacing (temporal and spatial), and speed of interaction to make themselves available to the other and remove or suspend distractions from the environment in order to improve care. We draw from the rich, qualitative descriptions found in earlier work that point to specific, observable practices in clinical encounters and identified empirical and theoretical work across a range of disciplines to expand our understanding of these practices. Ultimately, we identify and elaborate on 10 observable indicators of patient-clinician communication: engaging in shared turn taking, establishing rapport through discussion of off-task topics, pausing to allow the other ample time to speak, moderating the pace of spoken language, avoiding conversational interruptions, minimizing external interruptions, triaging topics as needed to create adequate time, expressing emotions, encouraging participation through inviting questions, and displaying open body language. These indicators work together to cocreate unhurried conversations.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".