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Record W2756880822 · doi:10.22230/jripe.2017v7n1a267

Editorial: Toward an Experiential Approach to Interprofessional Communication

2017· editorial· en· W2756880822 on OpenAlexaffvenue
Hassan Soubhi

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2017
Typeeditorial
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsExperiential learningMedicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

There is a famous quote from the movie Cool Hand Luke [1] when the prison warden says to Luke right after hitting him, "What we've got here is failure to communicate; some men you just can't reach."Aside from the entertaining drama, I keep two implications from this quotation: communication is more than information transfer, it is also about reach; and reach can be costly.A dictionary definition reveals several meanings for the word "reach": stretch out; touch or grasp by stretching; arrive at; get in contact; and interestingly enough, "succeed in having an effect on" [2].I lean toward the latter, simply because thinking of communication as exerting an influence offers a better chance of assessing that influence.Our goals must be smart and measurable.They must also consider local contexts.Communication is central in an interprofessional context.Problems arise when we treat communication mainly as an exchange of information and ignore all the subtle non-verbal signifiers: attitudes, body language, cultural norms, and institutional rules.Shannon and Weaver [3] offered a theory of communication as reliability in encoding and decoding information as a set of symbols.We usually conceive of communication as the transfer of a string of symbols with the assumption that the syntactic rules are known-we get the meaning if we can decode the string.This view is appealing.We see its usefulness in computers as they extend our minds every day.It becomes, however, particularly costly with specialization and the increased division of labour that is necessary for collective works.As it increases productivity, specialization also brings with it elaborate algorithms that only experts know.A higher level of energy must then be spent to encode and decode expert information-a cost that can be prohibitive in an interprofessional context, which calls for speed and efficiency in the integration and transfer of knowledge.So if interprofessional communication is more than information transfer, and if the division of labour increases both the productivity and the costs of communication, what can we do?How can we better understand interprofessional communication?First, we need to consider that an effective interprofessional group lowers the costs of communication.The human history of collective work is filled with successive attempts to reduce those costs and coordinate the exchange of energy-an important part of what makes or breaks collective work.For what do groups do in the real world?At a fundamental level, they exchange information and energy.In doing so, they establish connections between agents and objects: the connections can be physical (as in sharing the other end of the rope), symbolic (as in incentives), or mental (as in language and working memories in the brain).Group members not only exchange bits

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.023
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.053
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.002
Science and technology studies0.0070.006
Scholarly communication0.0130.008
Open science0.0050.003
Research integrity0.0230.029
Insufficient payload (model declined to judge)0.0140.012

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 head0.111
GPT teacher head0.599
Teacher spread0.489 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations2
Published2017
Admission routes2
Has abstractyes

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