Patient Navigation in Mothers at Risk for and Surviving with Breast/Ovarian Cancer: The Role of Children’s Ages in Program Utilization and Health Outcomes
Bibliographic record
Abstract
Background/Objectives: Many women at risk for and surviving with breast/ovarian cancer are simultaneously raising children. These women often experience unique challenges due to concurrent demands as both parents and patients with cancer. Community-based cancer control organizations offer vital patient navigation (PN), including psychoeducational services. Yet, little is known about how PN addresses these mothers’ comprehensive care needs. Methods: We examined PN program data from N = 1758 women served by a national cancer organization. Results: Out of the 69% of navigated women who were mothers, most were raising adult children only (age ≥ 18; 56%); however, 31% were mothers with young children only (age < 18), and 13% were mothers with both adult and young children (χ2 = 341.46, p < 0.001). While mothers with adult children reported poorer quality of life (QoL) than mothers with young children (physically unhealthy days, t = −2.2, df = 526, p < 0.05; total unhealthy days, t = −1.2, df = 533, p < 0.05), there were no significant differences in their PN experiences. For mothers with young children, a better QoL was associated with a lower genetic risk for cancer (r = −0.12) and a stronger sense of psychosocial empowerment (r = 0.10) (all p’s < 0.05). In an adjusted multivariate regression model of QoL, as empowerment increased, the influence of PN quality decreased (ß = −0.007, SE of ß = 0.00, p = 0.02), suggesting that strengthening mothers of young children’s sense of agency over their breast/ovarian cancer is critical to achieving overall well-being. Conclusions: CBO-led cancer control programming that supportively cares for mothers across their cancer journey can be essential to their QoL, especially for those who are raising minors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".