Role Conflict and Self‐Efficacy Among Employed Parents: Examining Complex Statistical Interactions
Bibliographic record
Abstract
In response to growing concerns with explaining how work and family interfere with each other and with statistical approaches that do not capture the way in which predictors interact, this study tested statistical interactions involving personal and social resources of 410 full‐time employed women and men. The results indicate that self‐efficacy is a strong predictor of family interfering with work (FIW) and work interfering with family (WIF). Gender moderates the relation between supervisor support and WIF moderates the impact of efficacy beliefs and instrumental support at home on FIW. Specifically, while supervisor support is negatively related to WIF in women and men, high levels of support more strongly affected men's perceptions of WIF. In low self‐efficacy men, high levels of support at home improved their perceptions of FIW but these perceptions worsened in women. These findings contrast with earlier research that focus predominantly on the predictive value of structural demands (for example, the number of hours worked per week and family size). This study shows that gender plays a critical yet intricate role as a predictor of the successful management of work and family roles: it is not gender per se but its interaction with personal and social variables that informs us about differences in the experience of employed parents.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".