The Work-Home Border Accounting System: A Function of Psychological Detachment
Bibliographic record
Abstract
COVID-19 transformed work arrangements by increasing the adoption of the work-from-home format. Work-from-home employees are susceptible to continuous job stressors and the permeability of the work-to-home border. Psychological detachment serves as the pathway to recovery from job stressors and for preventing the carryover of work thoughts into the home domain during non-work time. Therefore, this review presents theoretical and practical implications for work-to-home border management by integrating literature about psychological detachment and work-from-home. Work-home border accounting involves examining and minimizing conditions that inhibit psychological detachment, e.g., ICTs demand, overtime, telepressure, interruptions, etc., which are withdrawals from employees' energy or resource accounts, while facilitating psychological detachment through deposits which are non-work activities such as leisure, social support, exercises, etc. This article proposes the work-home border accounting system for managing the work-to-home border. The review suggests the resources employees accumulate through psychological detachment, e.g., wellbeing, life satisfaction, and work-life balance, and the deficits employees accrue if there is a shortfall of deposits required for psychological detachment, e.g., stress, low productivity, work-family conflict, etc. Hence, this article provides a coherent understanding of the processes necessary for psychological detachment during work-from-home.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".