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Record W2789061384 · doi:10.1080/10773525.2018.1436015

Use of information communication technology and stress, burnout, and mental health in older, middle-aged, and younger workers – results from a systematic review

2017· review· en· W2789061384 on OpenAlexaboutno aff
Gabriele Berg‐Beckhoff, Grace Nielsen, Eva Ladekjær Larsen

Bibliographic record

VenueInternational Journal of Occupational and Environmental Health · 2017
Typereview
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOBurnoutOccupational stressCochrane LibraryTechnostressCross-sectional studySystematic reviewMedicineInformation and Communications TechnologyMental healthClinical psychologyMEDLINEPopulationPsychologyGerontologyEnvironmental healthMeta-analysisPsychiatry

Abstract

fetched live from OpenAlex

The aim of this systematic review is to summarise quantitative studies in occupational settings observing the association between Information communication technology (ICT) and stress, and burnout, considering age as an effect modifier. A systematic review using PRISMA guidelines was conducted through the following bibliographic databases: PubMed, Web of Science, Psycinfo, and the Cochrane Library. Inclusion criteria were occupational settings and content relevant to our research question. Risk of bias was assessed using the Newcastle-Ottawa scale. Two interventional, 4 cohorts, and 29 cross-sectional studies were found. ICT use in occupational settings was associated with stress seen in cross-sectional studies, but not in interventional studies. There was a concordant association with ICT and burnout in different study designs. Overall, there were no linear trends between age and technostress. We suggest that the observed associations were mostly present in the middle-aged working population and that these associations need to be supported in further studies.

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)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.407
Teacher spread0.327 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations195
Published2017
Admission routes1
Has abstractyes

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