A qualitative exploration of supports and unmet needs of diverse young women with breast cancer
Bibliographic record
Abstract
BACKGROUND: Young women with breast cancer face different challenges than those faced by older women because of their age and life stage, yet few studies have focused on the different challenges faced by women from diverse populations. OBJECTIVE: To explore existing supports that are important during diagnosis and treatment and the unmet needs for information and support in young women with breast cancer. METHODS: We conducted 20 semistructured interviews in English with women aged 42 or younger who had been diagnosed with stage I-III invasive breast cancer within the previous 4 years. We recorded and transcribed the interviews and used collaborative group immersion/ crystallization to analyze data, identify emergent themes, and determine if there were differences by race/ethnicity. RESULTS: 20 participants, recruited from 9 US states and Canada, were interviewed, of whom 25% were Hispanic, 15% were black, 50% were white and non-Hispanic, and 10% were another race/ethnicity. Faith and/or spirituality and family were reported as important sources of support by many of the participants. Most of them lamented the inadequacy of their connections with other young survivors and also of supports for their family. Some recommended that young patients be provided with more information about: treatment-related physical and emotional changes; fertility and menopause; relationships after cancer; navigating work challenges; and transitioning into survivorship. None of these supports or recommendations was limited to a specific race/ethnicity or geographic region. LIMITATIONS: Small sample size, exclusion of non-English speakers. Conclusions Key informant interviews of young breast cancer survivors identified similar needs for education and support across various races/ethnicities and geographies. CONCLUSIONS: Key informant interviews of young breast cancer survivors identified similar needs for education and support across various races/ethnicities and geographies.
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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 | 0.002 | 0.000 |
| 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 | 0.001 |
| 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".