Reduced Psychosocial Well-Being among the Children of Women with Early-Onset Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Approximately 27% of female breast cancer patients are diagnosed before the age of 55, a group often comprising mothers with young children. Maternal psychosocial well-being significantly impacts these children's psychosocial well-being. This study assesses the well-being of children with mothers who have early-onset breast cancer. METHODS: We examined the eldest child (up to 15 years old) of women with nonmetastatic breast cancer (<55 years old, mean age: 40) enrolled in the mother-child rehab program 'get well together'. Using maternal reports on children's well-being (the Strengths and Difficulties Questionnaire; SDQ), we describe the prevalence of abnormally high SDQ scores and identify protective and risk factors via linear regression. RESULTS: The mean SDQ scores of 496 children (4-15 years old, mean age: 8) fell below the thresholds, indicating psychosocial deficits. However, most SDQ scores deviated negatively from the general population, especially for emotional problems, with one in ten children displaying high and one in five displaying very high deficits. Female sex, more siblings, a positive family environment and maternal psychosocial well-being were protective factors for children's psychosocial well-being. CONCLUSIONS: Children of mothers with breast cancer may benefit from improved maternal well-being and family support. Further research is needed to identify appropriate interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".