“I Beat Cancer to Feel Sick:” Qualitative Experiences of Sleep Disturbance in Black Breast Cancer Survivors and Recommendations for Culturally Targeted Sleep Interventions
Bibliographic record
Abstract
BACKGROUND: Sleep disturbance is common and distressing among cancer survivors. Black breast cancer survivors (BBCS) suffer disproportionately from sleep disturbance, yet there is limited research on how to address this issue. PURPOSE: This study aimed to understand the multifaceted experiences of sleep disturbance among BBCS and how to culturally target a mobile health (mHealth) intervention to improve sleep outcomes in BBCS. METHODS: Semi-structured interviews were conducted in a purposive sample of 10 BBCS. Interviews were audio-recorded, transcribed, and coded for key barriers to sleep and potential solutions to incorporate into behavioral interventions using NVivo 12. Inductive applied thematic analysis techniques were employed to identify emergent themes. RESULTS: Ten BBCS (mean age = 54, SD = 10) described their experiences of sleep disturbance with themes including: (1) barriers to quality sleep (e.g., cancer worry, personal responsibilities), (2) psychosocial impacts of sleep disturbance (e.g., fatigue, distress), and (3) commonly used strategies to improve sleep. The second section discusses suggestions for developing mHealth interventions to improve sleep for BBCS including: (1) feedback on an existing mHealth intervention and (2) intervention topics suggested by BBCS. CONCLUSIONS: Our findings highlight the challenges associated with sleep disturbance in BBCS. Participants report culturally targeted mHealth interventions are needed for BBCS who experience chronic sleep disturbance that affects their overall quality of life. These interventions should address coping with sleep-related issues relevant to many breast cancer survivors and BBCS (e.g., sexual intimacy, fear of cancer recurrence) and should incorporate intervention strategies acceptable to BBCS (e.g., prayer, meditation).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".