An Analysis of Latent Profiles of Father-Child Interaction: Classification Predictors and Differences in Children’s Socio-Emotional Development
Bibliographic record
Abstract
The purpose of this study was to (1) classify subgroups of father-child interaction based on the type of interaction activity (routine, learning, and play interaction), (2) examine the effects of socio-demographic factors, father’s psycho-emotional factors, and maternal factors on the different types of father-child interaction groups, and (3) analyze differences in socio-emotional development of first graders in elementary school according to the type of father-child interaction. Analysis of 1,469 families (mothers, fathers, and children) was conducted using latent profile analysis (research question 1), complex sample multinomial logistic regression (RQ 2), and complex sample general linear modeling (RQ 3). Samples originated from the eighth wave (2015) of the Panel Study on Korean Children (PSKC). The main results were as follows. First, three distinct latent groups of father-child interaction based on the quantitative level of daily interaction were found: high-interaction (HI, 7.85%), medium-interaction (MI, 51.73%), and low-interaction (LI, 40.42%). Second, factors such as father’s happiness, positive evaluation of work-family balance, and mother-child interaction level were significant correlates for the classification of father-child interaction. Third, first graders in the HI group showed the highest levels of self-esteem in comparison to the other two groups and reported a higher level of subjective happiness in comparison to the LI group. These results bring to attention the importance of father-child interaction affecting the outcomes of children’s socio-emotional development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".