PO-0385a Adverse Childhood Experiences In Alberta, Canada: A Population Based Study
Bibliographic record
Abstract
Objective Adverse childhood experiences (ACEs) are associated with poor health outcomes in adulthood. We developed ACE risk profiles using a domain-specific approach and examined ACEs as risk factors for diagnosed physical and mental health conditions. Method A computer-assisted telephone survey was conducted with a random sample of adults in Alberta, Canada. Eight questions were asked on adversity during childhood, based on the original ACE survey and modified to reflect the Canadian context and research methodology. Descriptive and multivariable analyses were conducted. Results Among the 1207 respondents, the majority were married or living common-law (65.8%), had completed post-secondary education (78.2%), and were Caucasian (86.2%) with a mean age of 52.4 years (SD=16.3). Approximately one-third (27.3%) experienced at least one type of abuse, and almost half (49.5%) experienced at least one form of household dysfunction. ACEs were highly interrelated. Sixty-three percent fell into the low risk profile, with the remaining 37% divided among the three higher risk profiles. Overall, ACE risk profile was significantly associated with diagnosed mental health condition/addiction and chronic pain, controlling for sociodemographic characteristics. Conclusion The ACE risk profile of ACEs in both the abuse domain and the household dysfunction domain conferred the greatest risk for poor health outcomes in adulthood. Given the interrelated of ACEs, a more comprehensive approach to conceptualization of ACEs is warranted. Results have implications for prevention of ACEs and recovery from ACEs to decrease disease burden. Strategies may include effective programs to prevent exposure to toxic stress and support nurturing and stable relationships for children and families.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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".