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Record W4387847156 · doi:10.1177/21695067231192866

Building Situation Awareness and Team Cohesion through Effective Information Sharing in a Distributed Team-Based Command and Control Scenario

2023· article· en· W4387847156 on OpenAlexaff
Chrissy M. Chubala, Aren Hunter, Lori Dithurbide, Heather F. Neyedli

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2023
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsDalhousie UniversityDefence Research and Development Canada
Fundersnot available
KeywordsCohesion (chemistry)Information sharingKnowledge managementComputer scienceGroup cohesivenessCommunity cohesionCommand and controlControl (management)PsychologySocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Within complex command and control domains, where teams are often geographically distributed and heterogeneous in expertise, the formulation of a course of action depends upon effective information sharing between teammates. The communication and development of situation awareness (SA) may both depend upon and help foster trust and cohesion among teammates. However, distributed and heterogeneous environments present challenges for the development of trust and cohesion. This study investigated the relationship between information sharing, SA, trust, and team cohesion in a distributed team-based emergency management scenario. Participants played the role of humanitarian aid experts, public health experts, and migration and social services experts responding to a potential outbreak of cholera following a natural disaster. The results of exploratory analyses provide preliminary support for a complex relationship between effective information sharing, the convergence of higher-level SA, and the development of team cohesion, and propose a roadmap for further exploration of this rich dataset.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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