Unraveling the after-hours dilemma: Consequences of overworking among teleworkers—A scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Telework, also referred to as telecommuting, remote work, flexible work, and virtual work, involves working from a location different from the traditional office and often uses online communication technologies. Despite the numerous advantages associated with teleworking, it also raises concerns about work-life balance and health implications due to working after hours (WAH). OBJECTIVE: This proposed study aims to understand the health consequences of teleworkers working beyond their scheduled hours. METHODS: This review will search seven online databases (APA PsycINFO, Medline, Embase, Scopus, Business Source Premier, CINAHL, and Sociological Abstracts) to gather relevant articles. The inclusion criteria will encompass peer-reviewed studies published from 2010 onwards, focusing on WAH among teleworkers and reporting mental and physical health consequences. The exclusion criteria will include non-peer-reviewed articles, grey literature, and studies involving patients with pre-existing conditions. DISCUSSION: This review will provide valuable insights into the mental and physical health consequences of WAH among teleworkers, underscoring the urgent need for strategies to mitigate these risks and promote overall well-being. Future efforts, including collaborations between researchers, industry leaders, and policymakers, can guide the development of targeted interventions and evidence-based policies that improve telework environments and support long-term worker health and productivity.
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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 | 0.075 | 0.075 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".