Mothers with Cancer: An Intersectional Mixed-Methods Study Investigating Role Demands and Perceived Coping Abilities
Bibliographic record
Abstract
Mothers with cancer report guilt associated with failing to successfully balance their parental roles and cancer. This study utilized a cross-sectional mixed-methods design and intersectional framework to investigate the multiple roles that mothers with cancer assume and their perceived coping ability. Participants included mothers diagnosed with any type or stage of cancer, in treatment or ≤3 years post-treatment, and experiencing cancer-related disability with a dependent child (<18 years, living at home). Participants completed a questionnaire battery, semi-structured interview, and optional focus group. Descriptive statistics, correlations, and thematic inductive analyses are reported. The participants’ (N = 18) mean age was 45 years (SD = 5.50), and 67% were in active treatment. Their role participation (M = 42.74, ±6.21), role satisfaction (M = 43.32, ±5.61), and self-efficacy (M = 43.34, ±5.62) were lower than the general population score of 50. Greater role participation and higher role satisfaction were positively correlated (r = 0.74, p ≤ 0.001). A qualitative analysis revealed that the mothers retained most roles, and that their quality of life depended on their capacity to balance those roles through emotion-focused and problem-focused coping. We developed the intersectional Role Coping as a Mother with Cancer (RCMC) model, which has potential research and clinical utility.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".