ArticleFamily relations2025
Exploring father-adolescent closeness: A random forest approach.
Article in Family relations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Most important predictors of father-child contact in the U.S. child welfare system: A machine learning approach.Child abuse & neglect · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Objective: This study demonstrates how machine learning, specifically random forest, can advance family science, particularly in studying father-child relationships. Background: Fatherhood research faces challenges with fathers' recruitment and retention, complex living arrangements, and lower response rates compared to mothers. Machine learning, a tool of artificial intelligence, effectively examines large and complex data sets, handles missing data, and identifies relationships between predictors and outcomes. Thus, machine learning can help mitigate the methodological challenges of studying fathers and father-child relationships. Method: We used random forest to predict adolescent-reported father-adolescent closeness in the Future of Families and Child Wellbeing Study ( Results: Fathers' residential status with the child was the strongest predictor of father-adolescent closeness. Using random forest results to inform variable selection, we demonstrated how random forest can enhance the development and performance metrics of regression models. Conclusion: This study highlights the utility of random forest for studying complex questions, such as how family contexts predict adolescents' perceptions of their father-child relationships. Implications: Random forest is a feasible and useful approach for applied family scientists to incorporate artificial intelligence into their research, moving the field in new and meaningful directions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.