Adverse Childhood Experiences and the Co‐occurrence of Poor Oral Health and Multimorbidity: Findings From the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: To investigate the extent of the association of adverse childhood experiences (ACEs) with co-occurring poor self-reported oral health (SROH) and multimorbidity in middle-aged and older adults, and whether these associations differ by age and sex. METHODS: This cross-sectional study used data from 27 765 adults aged 45-85 years from the first follow-up wave (2015-2018) of the Canadian Longitudinal Study on Aging (CLSA). Four categories were generated to assess co-occurring SROH and multimorbidity: (i) good SROH, no multimorbidity; (ii) poor SROH, no multimorbidity; (iii) good SROH, multimorbidity and (iv) poor SROH and multimorbidity. Age-and sex-stratified multinomial logistic regressions were used to examine associations of ACEs (e.g. childhood maltreatment, neglect, parental death, serious illness or separation) with co-occurring poor SROH and multimorbidity, adjusted for the confounders race/ethnicity, income, level of education, smoking status and alcohol consumption. RESULTS: Over a third of participants reported having multimorbidity (35.3%), 10.4% reported poor SROH, and almost 30% of participants had experienced at least one ACE. There was a gradient in the association between higher ACEs and each of the health outcome categories, with the greater odds being for the co-occurrence of poor SROH and multimorbidity (OR = 1.37, 95% CI: 1.30, 1.44). The associations between ACEs and adverse health outcomes in later life were significant across age groups and sexes, with middle-aged females demonstrating the strongest associations. CONCLUSIONS: ACEs are linked to an increased non-communicable chronic disease burden and poor oral health among middle-aged and older Canadians, highlighting the importance of prevention in early life and the focus on psychosocial factors over the life course for healthy aging.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| 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".