The Association between a History of Parental Addictions and Arthritis in Adulthood: Findings from a Representative Community Survey
Bibliographic record
Abstract
Aims. To examine the relationship between a history of parental addictions and the cumulative lifetime incidence of arthritis while controlling for age, sex, race, and four clusters of risk factors: (1) other adverse childhood experiences, (2) adult health behaviors (i.e., smoking, obesity, inactivity, and alcohol consumption), (3) adult socioeconomic status and (4) mental health. Materials and Methods. Secondary analysis of 13,036 Manitoba and Saskatchewan respondents of the population-based 2005 Canadian Community Health Survey. Sequential logistic regression analyses were conducted. Findings. After controlling for demographic characteristics, including age, gender, and race, respondents who reported a history of parental addictions had significantly higher odds of arthritis in comparison to individuals without ( OR=1.58 ; 95% CI 1.38–1.80). Adjustment for socioeconomic status, adult health behaviors, and mental health conditions had little impact on the parental addictions and arthritis relationship. The association between parental addictions and arthritis was substantially reduced when adverse childhood experiences ( OR=1.33 ; 95% CI 1.15–1.53) and all four groups of risk factors collectively ( OR=1.30 ; 95% CI = 1.12–1.51) were included in the analyses; however, the relationship remained statistically significant. Conclusions. A robust association was found between parental addictions and cumulative lifetime incidence of arthritis. This link remained even when controlling for four groups of potential risk factors.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".