Confirmatory factor analysis of adverse childhood experiences (ACEs) among a community-based sample of parents and adolescents
Bibliographic record
Abstract
BACKGROUND: Despite increased understanding of Adverse Childhood Experiences (ACEs), very little advancement has been made in how ACEs are defined and conceptualized. The current objectives were to determine: 1) how well a theoretically-derived ACEs model fit the data, and 2) the association of all ACEs and the ACEs factors with poor self-rated mental and physical health. METHODS: Data were obtained from the Well-Being and Experiences Study, survey data of adolescents aged 14 to 17 years (n = 1002) and their parents (n = 1000) in Manitoba, Canada collected from 2017 to 2018. Statistical methods included confirmatory factor analysis (CFA) and logistic regression models. RESULTS: The study findings indicated a two-factor solution for both the adolescent and parent sample as follows: a) child maltreatment and peer victimization and b) household challenges factors, provided the best fit to the data. All original and expanded ACEs loaded on one of these two factors and all individual ACEs were associated with either poor self-rated mental health, physical health or both in unadjusted models and with the majority of findings remaining statistically significant in adjusted models (Adjusted Odds Ratios ranged from 1.16-3.25 among parents and 1.12-8.02 among adolescents). Additionally, both factors were associated with poor mental and physical health. CONCLUSIONS: Findings confirm a two-factor structure (i.e., 1) child maltreatment and peer victimization and 2) household challenges) and indicate that the ACEs list should include original ACEs (i.e., physical abuse, sexual abuse, emotional abuse, emotional neglect, physical neglect, exposure to intimate partner violence (IPV), household substance use, household mental health problems, parental separation or divorce, parental problems with police) and expanded ACEs (i.e., spanking, peer victimization, household gambling problems, foster care placement or child protective organization (CPO) contact, poverty, and neighborhood safety).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".