Predicting wellbeing in empty-nesters: an ensemble machine learning approach with data-driven feature selection
Wael Khreich, Mona Ayoub · 2026
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Description
Khreich and Ayoub used in 2026 machine learning to predict wellbeing in empty-nest parents, those whose children have left home. Worked examples that turn prediction methods into psychology questions are scarce, so the pair used the empty nest as a demonstration case. They drew on a large European survey of over forty-three thousand parents aged 45 and older, matched the two parent groups fairly, and hunted for robust predictors among 77 candidates.
Khreich found the two groups barely differed in happiness or life satisfaction, even after matching. Health status and a sense of freedom and control predicted happiness most strongly. Life satisfaction drew on a wider set, spanning personal resources, social ties, trust in institutions, and social capital. Most predictors worked alike in both groups, with only a modest set of group-specific effects. The authors conclude wellbeing in later life rides on factors that cross family setups.
Catalog record
| CREATOR | Wael Khreich, Mona Ayoub |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 82 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |