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Record W4316468823 · doi:10.1038/s41598-023-27606-3

A desire for distraction: uncovering the rates of media multitasking during online research studies

2023· article· en· W4316468823 on OpenAlexafffund
Allison C. Drody, Effie J. Pereira, Daniel Smilek

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHuman multitaskingDistractionComputer sciencePsychologyData scienceCognitive psychology

Abstract

fetched live from OpenAlex

Interpretations of task performance in many cognitive studies rest on the assumption that participants are fully attentive to the tasks they agree to complete. However, with research studies being increasingly conducted online where monitoring participant engagement is difficult, this assumption may be inaccurate. If participants were found to be engaging in off-task behaviours while participating in these studies, the interpretation of study results might be called into question. To investigate this issue, we conducted a secondary data analysis across nearly 3000 participants in various online studies to examine the prevalence of one form of off-task behaviour: media multitasking. Rates of media multitasking were found to be high, averaging 38% and ranging from 9 to 85% across studies. Our findings broadly raise questions about the interpretability of results from online studies and urge researchers to consider the likelihood that participants are simultaneously engaging in off-task behaviours while completing online research tasks.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.394
GPT teacher head0.465
Teacher spread0.071 · 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 designBench or experimental
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

Citations23
Published2023
Admission routes2
Has abstractyes

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