Childhood Maltreatment as a Risk Factor for Arthritis: Findings From a Population‐Based Survey of Canadian Adults
Bibliographic record
Abstract
OBJECTIVE: To establish whether there is a relationship between the frequency and severity of different types of childhood maltreatment and adulthood arthritis. METHODS: Analysis of the 2012 Canadian Community Health Survey-Mental Health included 21,889 respondents ages ≥18 years. Severity and frequency of childhood physical abuse (CPA), and childhood sexual abuse (CSA), and the frequency of childhood exposure to intimate partner violence (CEIPV) were assessed by asking about "things that may have happened to you before you were 16 in your school, in your neighborhood, or in your family." Respondents were also asked about chronic conditions diagnosed by a health professional, including arthritis. Covariates were sociodemographic characteristics, health risk variables (e.g., obesity), mental disorders, and a count of other chronic conditions. Multivariate logistic regression analysis was used to examine associations between childhood maltreatment and arthritis. RESULTS: A total of 17.5% of respondents reported arthritis. A higher prevalence of arthritis was observed for those who had experienced severe and/or frequent childhood maltreatment (32% for CPA and 27% for both CSA and CEIPV). These relationships persisted after controlling for sociodemographic variables. After controlling for all covariates, arthritis remained independently associated with severe and/or frequent CPA (dose-response relationship) and frequent CEIPV. CONCLUSION: We found that the greater the frequency and severity of childhood maltreatment, the greater the magnitude of association with arthritis. This might reflect the role of the enduring immune and metabolic abnormalities and chronic inflammation associated with childhood maltreatment in the etiopathogensis of osteoarthritis (OA) or be an indicator of the role of joint injury in causing OA.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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".