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Record W7112020746

Exploring the relationship between remote e-working and work-related well-being.

2017· article· en· W7112020746 on OpenAlexaff

Bibliographic record

VenuePure (Coventry University) · 2017
Typearticle
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsThematic analysisSample (material)Scale (ratio)Work (physics)Qualitative researchData collectionThematic map
DOInot available

Abstract

fetched live from OpenAlex

Remote e-working refers to work conducted at anyplace and anytime and enabled by ICTs. This qualitative study aims to explore how remote e-working may influence five dimensions of work-related well-being (i.e., affective, social, cognitive, psychosomatic, and professional). A semi-structured interview method was employed on a sample of thirty-nine e-workers (22 female, Mage= 46.86) working for a UK software development company. Thematic analysis identified five common themes (e.g., exercise, eating habits and associated-health outcomes). Preliminary findings expand our theoretical knowledge suggesting that remote e-working relates to each of well-being dimensions. Practical implication is the development of a new scale measuring e-well-being. <br/><br/>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.114
GPT teacher head0.301
Teacher spread0.186 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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