Felt deficits in time with children: Individual and contextual factors across 27 European countries
Bibliographic record
Abstract
A sizeable portion of parents say they lack time with children-an important social problem given that time strains link to parental well-being. Extending perspectives on the demands and rewards of parenting beyond the individual level, we provide a contextual-level window onto mothers' and fathers' time strains. Based on data from the European Quality of Life Survey 2016/17 (n = 5,898), we analyze whether parents feel they spend enough time caring for their children using multilevel models. We first observe that country context matters in that perceptions of time only moderately or weakly relate to hours with children across countries, especially for fathers, suggesting varying social expectations across Europe. Second, in multivariate analyses examining micro- and macro-level factors, we show that at the individual level, feeling too little time with children is more frequent among fathers and those who work more hours, even when controlling for estimated weekly hours spent caring for children. At the country level, parents' time strain is higher in countries where employees have less time and place flexibility, typically in Central and Eastern as well as Southern Europe. Gender norms matter as well. Extending contextual perspectives, we argue that how gender-work-family regimes color felt time strain is a promising future research direction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".