Population attributable fractions of adolescent health and well-being outcomes associated with adverse childhood experiences in a provincially representative sample in Ontario, Canada
Bibliographic record
Abstract
Background: It is well known that Adverse Childhood Experiences (ACEs) are associated with poor health and well-being outcomes among adult samples. However, there are notable gaps in examining these relationships among youth. Objective: The objectives were to examine: a) the prevalence of an expanded list of ACEs among adolescents, b) ACEs sex differences, c) associations between ACEs and several adolescent health and at-risk behavioural outcomes, and d) the population attributable fractions (PAFs) for three ACE groupings (i.e., child maltreatment, household challenges, and peer victimization). Study design: Cross-sectional. Participants and setting: Data were from the provincially-representative, cross-sectional 2014 Ontario Child Health Study (N = 6537 dwellings, response rate = 50.8%). One randomly selected child aged 14–17 years old (n = 2910) from each household was included. Methods: The majority of measures (nine ACEs and six health and well-being outcomes) were self-reported (three household challenges ACEs and physical health were collected from parents/caregivers). Descriptive statistics estimated the prevalence of ACEs for the sample and by sex. Logistic regressions tested associations between individual ACEs and seven outcomes. Population attributable fractions (PAFs) were computed for three ACE groupings with each outcome. Findings: ACEs prevalence ranged from 1.8% to 47.4% with several noted sex differences. Each ACE was associated with four or more studied outcomes. PAFs ranged from 3.5% to 47.8%, varying for each ACEs grouping. Conclusion: The significant associations and estimated proportions of poor adolescent outcomes attributed to ACEs indicate that identifying approaches aimed at preventing these experiences could have a substantial impact on youth health and well-being.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".