Prevalence of adverse childhood experiences among individuals aged 45 to 85 years: a cross-sectional analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Population-level prevalence estimates for a broad range of adverse childhood experiences (ACEs), which are known to affect health across the lifespan, are currently unavailable in Canada. The purpose of this study was to estimate the prevalence of individual ACEs by sociodemographic factors among middle-aged and older adults. METHODS: Data for this cross-sectional analysis were obtained from the first follow-up (2015-2018) of the Canadian Longitudinal Study on Aging (baseline recruitment from 2011 to 2015). Participants included individuals aged 45-85 years and residing in the community in the 10 Canadian provinces. Exposure to ACEs was assessed using a retrospective, self-report questionnaire. Logistic regression was used to obtain the adjusted prevalence estimates of ACEs within groups formed by the sociodemographic characteristics and each variable was adjusted for all other sociodemographic variables. RESULTS: Of the 44 817 participants in the first follow-up, 61.6% (weighted) reported exposure to at least 1 ACE. Exposure to physical abuse (weighted prevalence of 25.7%), intimate partner violence (22.4%) and emotional abuse (21.8%) were the most prevalent types of ACEs. Individuals younger than 65 years (born in 1950-1969), with no postsecondary education or education below a bachelor's degree, or with annual household income less than $20 000 reported greater exposure to ACEs. Reporting for many ACEs was higher among women and those of nonheterosexual orientation. Overall, British Columbia, Alberta, Manitoba, Ontario and Quebec reported relatively higher prevalence for several examined categories of ACEs. INTERPRETATION: Adverse childhood experiences were highly prevalent across all demographic groups with substantial heterogeneity in the distribution among the middle and older age population. The high prevalence of ACEs and their potential negative consequences on health and well-being emphasize the need to develop and promote trauma-informed care to assist individuals affected by ACEs.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| 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".