Does Work-Life Reconciliation Increase Employee Retention? The Mediating Role of Employees’ Perceived Stress
Bibliographic record
Abstract
Today, retaining skilled and talented employees is one of the main concerns of organizations. To this end, various policies have been considered in recent years, including policies to reconcile work with personal life. We sought to investigate the effect of work-life reconciliation on employee retention while considering the mediating role of employees’ perceived stress. In 2023, we surveyed a sample of Quebec employees who are caring for young children or other family members. In general, work-life reconciliation policies significantly increase employee retention. We also studied how employees’ perceived stress, due to work-life conflict and insufficient annual income, mediate the effect of work-life reconciliation on employee retention. Although caring for children under 18 or other family members increases employees’ perceived stress, it does not directly affect employee retention. In sum, we found that employee retention can be increased through policies that promote work-life reconciliation and thereby reduce perceived stress. Our findings have important implications and may help managers and employees implement policies to reconcile work with personal life, decrease stress, and thus increase employee retention.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".