Examining the Antecedents of Working After Hours among Teleworkers: A Scoping Review Protocol
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".