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Examining the Antecedents of Working After Hours among Teleworkers: A Scoping Review Protocol

2025· review· en· W4412159055 on OpenAlexaff
Kishana Balakrishnar, Bao-Zhu Stephanie Long, Luke Anthony Fiorini, Aaron Howe, Ali Bani‐Fatemi, Basem Gohar, Behdin Nowrouzi‐Kia

Bibliographic record

VenueInternational Journal of Occupational and Environmental Safety · 2025
Typereview
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsProtocol (science)PsychologyApplied psychologyProcess managementOperations managementKnowledge managementEngineeringComputer scienceMedicineAlternative medicine

Abstract

fetched live from OpenAlex

There has been a significant rise in telework since the onset of the COVID-19 pandemic. Telework offers several benefits, such as greater flexibility and increased productivity. It also presents challenges such as increased after-hours work, which can negatively impact workers' physical and mental wellbeing. This scoping review aims to identify the antecedents of after-hours work among teleworkers. Online databases, including Medline via OVID, Embase via OVID, APA PsycINFO via OVID, International Bibliography of Social Sciences (IBSS) via ProQuest, Sociological Abstracts via ProQuest, Business Source Premier via EBSCOhost, and CINAHL via EBSCOhost will be searched to gather literature on factors affecting after-hours work among teleworkers. The inclusion criteria include study participants aged 18 or older, part of the working population, and individuals teleworking for at least six months. Additionally, the studies must be empirical, peer-reviewed, discuss the antecedents of after-hours work, and be published from 2010 to 2024. The findings from this study will guide organisations and healthcare professionals in developing strategies to reduce after-hours work among individuals who telework, thereby improving their overall health and wellbeing. The registration number for this scoping review on Open Science is (DOI 10.17605/OSF.IO/6A7M9).

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.075
GPT teacher head0.395
Teacher spread0.320 · 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 designOther design
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

Citations2
Published2025
Admission routes1
Has abstractyes

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