Long-Term Mental Health Outcomes in Mothers and Siblings of Children With Cancer: A Population-Based, Matched Cohort Study
Bibliographic record
Abstract
PURPOSE: Although a diagnosis of childhood cancer can have a profound effect on the entire family unit, its impact on the long-term mental health of family members is not well characterized. METHODS: A provincial childhood cancer registry in Ontario, Canada, was linked to birth records to identify separate population-based cohorts of mothers and siblings of children diagnosed with cancer between 1998 and 2014. The mother and sibling cohorts were matched to corresponding population controls and linked to health services data. The rate of mental health-related outpatient visits (family physician, psychiatrist) and the incidence of severe psychiatric events (psychiatric emergency department visit, psychiatric hospitalization, suicide) were compared between mothers and siblings and their controls. Possible predictors of mental health outcomes were examined, including demographics, characteristics of the cancer-affected child, and cancer treatment. RESULTS: < .0001). The risk of severe psychiatric events was not increased in either cohort. Mother and sibling demographic factors associated with increased risk of adverse mental health included younger maternal age at cancer diagnosis, low socioeconomic status, and rural residence among mothers and older sibling age among siblings. Treatment-related variables pertaining to the cancer-affected child were not associated with mental health outcomes. Mental health outcomes clustered within families. CONCLUSION: Both mothers and siblings experience elevated and prolonged need for mental health-related health care as compared with the general population. Demographic risk factors predict subpopulations at highest risk. Increased psychosocial support for family members during and after cancer therapy is warranted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".