“You really need a whole community”: a qualitative study of mothers’ need for and experiences with childcare support during cancer treatment and recovery
Bibliographic record
Abstract
PURPOSE: A cancer diagnosis poses unique challenges for moms with young children who must balance illness-management alongside existing paid (e.g., employment) and unpaid (e.g., domestic/caregiving) work. The goal of this study was to improve understanding of the support needs of mothers living with cancer and their experiences receiving psychosocial and childcare support from a community organization, the Nanny Angel Network (NAN). METHODS: Mothers who accessed NAN services during their cancer treatment and/or recovery (N = 20) participated in qualitative semi-structured interviews. Thematic analysis was used to inductively and deductively identify emerging patterns in the data and theoretical abduction was applied to further interpret participants' accounts using a feminist political economy framework. RESULTS: Participants expressed how balancing the demands of patienthood and parenthood was challenging and how cancer treatment created new needs for support with care work. Mothers explained that NAN offered indispensable family-centered support largely missing from the health care system, promoting improved physical, psychosocial, and relational health for them and their families. While accessible from a cost-perspective, participants identified different pathways, including awareness, cross-system collaboration, and stable funding, that limited timely access to NAN. CONCLUSION: Access to family-centered care, such as that offered through NAN, was vital to the health and healing of the study participants and their families. Improved collaboration with and investment in community organizations like NAN that have a strong infrastructure to support moms living with cancer offers a practical, feasible, and immediate solution to help address some of the distinct challenges this population faces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".