Offspring of parents with chronic pain
Bibliographic record
Abstract
Offspring of parents with chronic pain may be at risk for poorer outcomes than offspring of healthy parents. The objective of this research was to provide a comprehensive mixed-methods systematic synthesis of all available research on outcomes in offspring of parents with chronic pain. A systematic search was conducted for published articles in English examining pain, health, psychological, or family outcomes in offspring of parents with chronic pain. Fifty-nine eligible articles were identified (31 population-based, 25 clinical, 3 qualitative), including offspring from birth to adulthood and parents with varying chronic pain diagnoses (eg, mixed pain samples, arthritis). Meta-analysis was used to synthesize the results from population-based and clinical studies, while meta-ethnography was used to synthesize the results of qualitative studies. Increased pain complaints were found in offspring of mothers and of fathers with chronic pain and when both parents had chronic pain. Newborns of mothers with chronic pain were more likely to have adverse birth outcomes, including low birthweight, preterm delivery, caesarian section, intensive care admission, and mortality. Offspring of parents with chronic pain had greater externalizing and internalizing problems and poorer social competence and family outcomes. No significant differences were found on teacher-reported externalizing problems. The meta-ethnography identified 6 key concepts (developing independence, developing compassion, learning about health and coping, missing out, emotional health, and struggles communicating with parents). Across study designs, offspring of parents with chronic pain had poorer outcomes than other offspring, although the meta-ethnography noted some constructive impact of having a parent with chronic pain.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".