Fathers and Child Neglect
Bibliographic record
Abstract
OBJECTIVE: To examine the association between father involvement and child neglect. DESIGN: Cohort study. SETTING: Participants were recruited from an inner-city pediatric primary care clinic and a clinic for children at risk for human immunodeficiency virus infection in a teaching hospital. PARTICIPANTS: Mothers and fathers or father figures, and 244 five-year olds participating in a longitudinal study. MAIN OUTCOME MEASURES: Child neglect measured via home observation, a videotaped mother-child interaction, and child protective services reports. RESULTS: A father or father figure was identified for 72% of the children. Rates of neglect ranged between 11% and 30%. Father absence alone was not associated with neglect. However, in families with an identified and interviewed father, a longer duration of involvement (P<.01), a greater sense of parenting efficacy (P<.01), more involvement with household tasks (P<.05), and less involvement with child care (P<.05) were associated with less neglect. The overall model explained 26.5% of the variance in neglect. CONCLUSIONS: There is substantial involvement of fathers in a subset of this high-risk sample, although more than a quarter of the children lacked a father or father figure. The mere presence of a father did not significantly influence the neglect of the children; rather, the nature of his involvement did. Fathers who felt more effective as parents were less likely to have neglected their children. A greater sense of efficacy may reflect parenting skills and be important in enhancing the contribution of fathers to their children's well-being. Pediatric health care providers can play a valuable role in enhancing the involvement and skills of fathers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".