“Learning the Lingo”: A Grounded Theory Study of Telephone Talk in Clinical Education
Bibliographic record
Abstract
PURPOSE: Workplace-learning literature has focused on doing, but clinical practice also involves talking. Clinicians talk not only with patients but also about patients with other health professionals, frequently by telephone. The authors examined how the underexplored activity of work-related telephone talk influences physicians' clinical education. METHOD: Using constructivist grounded theory methodology, the authors conducted 17 semistructured interviews with physicians-in-training from various specialties and training levels from two U.S. academic health centers between 2015 and 2017. They collected and analyzed data iteratively using constant comparison to identify themes and explore their relationships. They used theoretical sampling in later stages until sufficiency was achieved. RESULTS: Residents and fellows reported speaking via telephone regularly to facilitate patient care and needing to tailor their talk to the goal(s) of the conversation and their conversation partners. Three common conversational situations highlighted the interplay of patient care context and conversation and created productive conversational tensions that influenced learning positively: experiencing and dealing with (1) power differentials, (2) pushback, and (3) uncertainty. CONCLUSIONS: Telephone talk contributes to postgraduate clinical education. Through telephone talk, physicians-in-training learn how to talk; they also learn through talk that is mediated by productive conversational tensions. These tensions motivate them to modify their behavior to minimize future tensions. When physicians-in-training improve how they talk, they become better advocates for their patients and more effective at promoting patient care. Preparing residents to deal with power differentials, pushback, and uncertainty in telephone talk could support their learning from this ubiquitous workplace activity.
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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 | 0.054 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| 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".