In Reply: Where Reshaping Communications in Healthcare Service Begins
Bibliographic record
Abstract
Any act of communication involves decision-making: what to say, how to say it, what to omit.Our biggest decision in writing "Improving How Clinicians Communicate with Patients: An Integrative Review and Framework" was to focus on the communication of clinicians rather than giving equal attention to patients' communication.To be sure, patients co-create the healthcare service experience, including its communications, as our article discusses.However, we saw our greatest potential value in helping clinicians envision how they can improve their own communications: verbal, nonverbal, and listening.Our article's Figure 2 presents the dynamic interplay between clinician and patient communication, a dyad rich with potential for further academic research.Both sides of that dyad merit much more study, and service researchers are well positioned to lead the way.The commentaries by Joseph Jacobson and by Jonah Berger and Grant Packard enrich the content of our paper, and we greatly value their care and thought in responding to it.An experienced medical oncologist, Jacobson has had many emotional, difficult conversations in sharing bad news with patients and family members.It is gratifying that our article resonated with him.Marketing professors Berger and Packard, leading communications researchers, offer excellent suggestions for future work, and we appreciate their affirmative remarks.To extend the dialog, we now reply to those two commentaries.
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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 | 0.011 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.127 | 0.121 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".