Identifying risk and protective factors in the transmission of chronic pain from mothers to children: a longitudinal cohort study
Bibliographic record
Abstract
ABSTRACT: Chronic pain is common and can significantly affect the individual and their family. Indeed, children have an increased risk of developing chronic pain when one or both of their parents have it. However, many children of parents with chronic pain do not report pain problems. The aim of this longitudinal study was to identify psychosocial factors throughout childhood that either increased or decreased the odds for chronic pain among children of mothers with and without chronic pain. Participants were 1128 mother-child dyads from a community-based cohort. Mothers self-reported on their chronic pain when children were 5 and 8 years and on potential risk and protective factors (anxiety and depressive symptoms, parenting practices, social support, coping, and optimism) at various time points between child ages 8 and 11 years. Children self-reported on their chronic pain at 13 years and on potential protective factors (optimism, connections with adults and peers, and community engagement) at 12 years. Logistic regression and moderation analyses demonstrated that children had increased odds for chronic pain at 13 years when their mothers reported both chronic pain and greater anxiety symptoms or ineffective parenting practices earlier in childhood. No protective factors moderated the association between mother-child chronic pain; however, greater child optimism and connections with adults at 12 years lowered the odds of chronic pain for all children, regardless of maternal chronic pain. These findings highlight factors that can be targeted in prevention efforts to mitigate the risk of chronic pain, but future research is needed to explore additional protective 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".