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Record W4386169029 · doi:10.1177/10946705231194075

Commentary: Using Language to Improve Health

2023· article· en· W4386169029 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Service Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsYork University
Fundersnot available
KeywordsPersuasionHealth communicationService (business)Cover (algebra)Quality (philosophy)PsychologyHealthcare serviceDomain (mathematical analysis)Work (physics)Public relationsHealth careComputer scienceKnowledge managementBusinessMarketingSocial psychologyCommunicationPolitical science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.267
GPT teacher head0.629
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it