There’s Two Sides to Every Tale: Enabling – Enclosing Work-Life Policies and Autonomy Satisfaction
Bibliographic record
Abstract
Work-life policies are offered to employee to help manage conflicting demands stemming from work and life. Yet findings on the actual outcomes of such policies are mixed. Indeed, most research tends to bundle different work-life policies instead of taking into consideration their differential nature. One exception is the enabling – enclosing conceptualisation of work-life policies (Bourdeau et al., 2019). In this paper, a series of four cross-sectional studies with various samples of potential and actual work-life policies users was conducted, in order to 1) empirically validate that work-life policies can be classified using the enabling – enclosing proposition; 2) investigate how the use of enabling, mid-range and enclosing policies relates with various intraindividual outcomes; and 3) integrate the self-determination perspective (Ryan & Deci, 2017) in order to explore the mediating role of autonomy satisfaction in these relationships. Confirmatory factorial analyses in all 4 studies confirmed the 3-factor model, both for projected use (Study 1, N = 292; Study 2, N = 168) and actual use (Study 3, N = 284; Study 4, N = 251) of work-life policies. Furthermore, structural equation modeling analysis showed that higher use of enabling policies leads to higher levels of satisfaction with life, career, and work-life balance, whereas higher use of enclosing policies leads to higher levels of burnout and turnover intentions, both directly (Study 3) and through the mediating role of autonomy satisfaction (Study 4). We discuss theoretical and practical implications of this research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".