Transmission of risk from parents with chronic pain to offspring: an integrative conceptual model
Bibliographic record
Abstract
Offspring of parents with chronic pain are at increased risk for pain and adverse mental and physical health outcomes (Higgins et al, 2015). Although the association between chronic pain in parents and offspring has been established, few studies have addressed why or how this relation occurs. Identifying mechanisms for the transmission of risk that leads to the development of chronic pain in offspring is important for developing preventive interventions targeted to decrease risk for chronic pain and related outcomes (eg, disability and internalizing symptoms). This review presents a conceptual model for the intergenerational transmission of chronic pain from parents to offspring with the goal of setting an agenda for future research and the development of preventive interventions. Our proposed model highlights 5 potential mechanisms for the relation between parental chronic pain and pediatric chronic pain and related adverse outcomes: (1) genetics, (2) alterations in early neurobiological development, (3) pain-specific social learning, (4), general parenting and family health, and (5) exposure to stressful environment. In addition, the model presents 3 potential moderators for the relation between parent and child chronic pain: (1) the presence of chronic pain in a second parent, (2) timing, course, and location of parental chronic pain, and (3) offspring's characteristics (ie, sex, developmental stage, race or ethnicity, and temperament). Such a framework highlights chronic pain as inherently familial and intergenerational, opening up avenues for new models of intervention and prevention that can be family centered and include at-risk children.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".