Childhood Physical Abuse Casts a Very Long Shadow: Physical and Mental Illness Among Older Adults
Bibliographic record
Abstract
Abstract A burgeoning literature indicates adverse childhood experiences (ACEs) are associated with chronic illness. Most research, to date, has not focused on health outcomes among older adults. The objectives of the current study were to identify the prevalence and adjusted odds of two mental health and six physical health conditions among survivors of childhood physical abuse (CPA) who were aged 60 and older (n=409) in comparison to their peers who had not been physically abused (n=4,659). Data were drawn from a representative sample of older British Columbians in the Canadian Community Health Survey. Logistic regression analyses took into account sex, race, age, immigration status, marital status, education, income, smoking, obesity, binge drinking and number of other ACEs. For 3 health outcomes, CPA survivors had adjusted odds ratio more than twice that of their peers (Anxiety OR=2.22; 95% CI=1.46, 3.38; Depression OR=2.17; 95% CI=1.57, 3.01; COPD OR=2.03; 95% CI=1.40, 2.94). For CPA survivors, the adjusted odds ratios were more than 50% higher for cancer (OR=1.71; 95% CI=1.31, 2.24), migraine (OR=1.67; 95% CI=1.15, 2.45) and debilitating chronic pain (OR=1.58; 95% CI=1.22, 2.03), and 33% higher for arthritis (OR=1.33; 95% CI=1.05, 1.69). CPA was not significantly associated with either heart disease or diabetes (p>.05). The association between CPA and two mental health and four physical health outcomes remained significant, even after controlling for sociodemographic characteristics, health behaviors and other ACEs. Further research is needed to investigate potential pathways through which childhood physical abuse is linked to a wide range of chronic later-life health problems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".