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Record W2060260877 · doi:10.1177/154193121005401952

Does Teaming up Make You Less Vulnerable to Task Interruption?

2010· article· en· W2060260877 on OpenAlexaff
Sébastien Tremblay, François Vachon, Daniel Lafond, Helen M. Hodgetts

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsDefence Research and Development CanadaUniversité Laval
Fundersnot available
KeywordsHuman multitaskingTask (project management)DyadControl (management)Overhead (engineering)Computer scienceTask switchingQuality (philosophy)Process managementPsychologyApplied psychologyCognitive psychologySocial psychologyBusinessEngineering

Abstract

fetched live from OpenAlex

Omnipresent in everyday multitasking environments task interruptions are usually detrimental to individual performance. Here, we examined whether teaming up renders an individual less vulnerable to interruptions in complex and dynamic situations. We employed a microworld to simulate command and control in a crisis management situation and to examine the relative impact of interruptions on operators working in a functional dyad versus operators working alone. While task interruption was detrimental to efficacy in supervisory control of both single and team interrupted operators, the latter were less vulnerable than the former. However, teaming up did not translate into faster resumption time, a consequence of the overhead attributable to coordination and communication requirements of collaborative work. These findings suggest that in complex and dynamic environments working in a small team confers more resistance to task interruption than working alone and speed of interruption recovery is no guarantee of quality of recovery.

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)0.002
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 score0.283
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.096
GPT teacher head0.357
Teacher spread0.261 · 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
Published2010
Admission routes1
Has abstractyes

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