The Consequences of a Work-Family (Im)balance: From the Point of View of Employers and Employees
Bibliographic record
Abstract
Finding the right balance between the different spheres of life of an individual, especially in the case of balancing work and family, requires a variety of measures and good cooperation from all stakeholders–employees, employers, trade unions, local communities and the state. A work-family balance (WFB) has a number of positive consequences, while an imbalance will have negative consequences for both employees and the organisations and society in general. The aim of this paper is to present the results of two studies comparing the positive consequences of WFB and the negative consequences of a work-family imbalance as perceived by employers and employees in Slovenia. The results of the surveys showed, firstly, that employers and employees recognize similar (but not identical) consequences of WFB, secondly, that the WFB of employees is better in companies where both stakeholders share a similar perception of consequences, and thirdly, that the perception of negative consequences of poor WFB differs the most between the organizations offering employees good possibilities for WFB and the organizations in which managers invested little or no effort in helping their employees balance their work and family lives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".