Bibliographic record
Abstract
Communication plays an integral role in service interactions and language shapes how service agents talk to customers, salespeople talk to prospects, and chatbots talk to consumers. But as Danaher, Berry, Howard, Moore, and Attai (2023) note, given healthcare’s impact on quality of life, it’s a particularly important domain to study effective communication. Their useful review and framework should help medical professionals improve patient interactions and encourage future research. That said, one paper can only cover so much ground, and there are several additional areas that deserve further attention. Building on their framework, we offer some additional areas for future work, including how to use language to better understand patients, how communication mediums (e.g., writing vs. speaking or online portals vs. email) shape what gets communicated, and how effective communication depends on the interaction’s goals (e.g., persuasion vs. medical adherence).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.068 | 0.073 |
| Insufficient payload (model declined to judge) | 0.018 | 0.018 |
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".