Adverse Childhood Experiences and Protective Factors With School Engagement
Bibliographic record
Abstract
OBJECTIVES: To determine the associations of adverse childhood experiences (ACEs) and protective familial and community factors with school performance and attitudes in children ages 6 to 17. METHODS: tests and logistic regressions assessed the relationships between ACEs and school outcomes, PFs and school outcomes, and both ACEs and PFs and school outcomes, adjusting for sex, age, race, ethnicity, and maternal education. RESULTS: Each negative school outcome is associated with higher ACE scores and lower PF scores. After adding PFs into the same model as ACEs, the negative outcomes are reduced. The strongest PF is a parent who can talk to the child about things that matter and share ideas. CONCLUSIONS: As children's ACE scores increase, their school performance and attitudes decline. Conversely, as children's PF scores increase, school outcomes improve. Pediatric providers should consider screening for both ACEs and PFs to identify risks and strengths to guide treatment, referral, and advocacy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".