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Record W2138434743 · doi:10.1177/1049732309338282

Communication Channels in General Internal Medicine: A Description of Baseline Patterns for Improved Interprofessional Collaboration

2009· article· en· W2138434743 on OpenAlexaff
Lesley Gotlib Conn, Lorelei Lingard, Scott Reeves, Karen‐Lee Miller, Ann Russell, Merrick Zwarenstein

Bibliographic record

VenueQualitative Health Research · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreMichener InstituteUniversity of Toronto
Fundersnot available
KeywordsAsynchronous communicationSpecialtyHealth careCategorizationHealth communicationDiversity (politics)Baseline (sea)PopulationMedical educationPsychologyNursingMedicineFamily medicineComputer scienceSociologyCommunication

Abstract

fetched live from OpenAlex

General internal medicine (GIM) is a communicatively complex specialty because of its diverse patient population and the number and diversity of health care providers working on a medicine ward. Effective interprofessional communication in such information-intensive environments is critical to achieving optimal patient care. Few empirical studies have explored the ways in which health professionals exchange patient information and the implications of their chosen communication forms. In this article, we report on an ethnographic study of health professionals' communication in two GIM wards through the lens of communication genre theory. We categorize and explore communication in GIM into two genre sets-synchronous and asynchronous-and analyze the relationship between them. Our findings reveal an essential relationship between synchronous and asynchronous modes of communication that has implications for the effectiveness of interprofessional collaboration in this and similar health care settings, and is intended to inform efforts to overcome existing interprofessional communication barriers.

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 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.340
GPT teacher head0.655
Teacher spread0.316 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations84
Published2009
Admission routes1
Has abstractyes

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