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Record W4378716125 · doi:10.2196/45179

Conceptualizing Interprofessional Digital Communication and Collaboration in Health Care: Protocol for a Scoping Review

2023· review· en· W4378716125 on OpenAlexvenueno aff
Kim Nordmann, Stefanie Sauter, Patricia Möbius-Lerch, Marie-Christin Redlich, Michael Schaller, Florian Fischer

Bibliographic record

VenueJMIR Research Protocols · 2023
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careDigital healthOperationalizationCINAHLPsycINFONursingContext (archaeology)Knowledge managementeHealthMedicineMedical educationMEDLINEPsychologyComputer sciencePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Effective communication and collaboration among health professionals are essential prerequisites for patient-centered care. However, interprofessional teams require suitable structures and tools to efficiently use their professional competencies in the service of high-quality care appropriate to the patient's life situation. In this context, digital tools potentially enhance interprofessional communication and collaboration and lead to an organizationally, socially, and ecologically sustainable health care system. However, there is a lack of studies systematically assessing the critical factors for successfully implementing tools for digitally supported interprofessional communication and collaboration in the health care setting. Furthermore, an operationalization of this concept is missing. OBJECTIVE: The aim of the proposed scoping review is to (1) identify factors influencing the development, implementation, and adoption processes of digital tools for interprofessional communication in the health care sector and (2) analyze and synthesize the (implicit) definition, dimensions, and concepts of digitally supported communication and collaboration among health care professionals in the health care setting. Studies focusing on digital communication and collaboration practices among health care professionals, including medical doctors and qualified medical assistants, in any health care setting will be included in this review. METHODS: To address these objectives, an in-depth analysis of heterogeneous studies is needed, which is best achieved through a scoping review. Within this proposed scoping review, which adheres to the Joanna Briggs Institute methodology, 5 databases (SCOPUS, CINAHL, PubMed, Embase, and PsycInfo) will be searched for studies assessing digital communication and collaboration among various health care professionals in different health care settings. Studies focusing on health care providers or patient interaction through digital tools and non-peer-reviewed studies will be excluded. RESULTS: Key characteristics of the studies included will be summarized through descriptive analysis, using diagrams and tables. We will synthesize and map the data and conduct a qualitative in-depth thematic analysis of definitions and dimensions of interprofessional digital communication and collaboration among health care and nursing professionals. CONCLUSIONS: Results from this scoping review may help in establishing digitally supported collaborations between various stakeholders in the health care setting and successfully implementing new forms of interprofessional communication and collaboration. This could facilitate the transition to better coordinated care and encourage the development of digital frameworks. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45179.

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)0.118
metaresearch head score (Gemma)0.116
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.118
Threshold uncertainty score0.623

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1180.116
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0110.014
Bibliometrics0.0230.022
Science and technology studies0.0060.006
Scholarly communication0.0100.008
Open science0.0060.010
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0490.008

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 head0.607
GPT teacher head0.770
Teacher spread0.163 · 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
GenreProtocol

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

Citations14
Published2023
Admission routes1
Has abstractyes

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