Racial differences in treatment adherence and response to acupuncture and cognitive behavioral therapy for insomnia among Black and White cancer survivors
Bibliographic record
Abstract
BACKGROUND: Racial disparities in sleep are well-documented. However, evidence-based options for addressing these disparities are lacking in cancer populations. To inform future research on sleep interventions, this study aims to understand racial differences in treatment responses to acupuncture and cognitive behavioral therapy for insomnia (CBT-I) among Black and White cancer survivors. METHODS: We conducted a secondary analysis of a comparative effectiveness trial evaluating acupuncture versus CBT-I for insomnia in cancer survivors. We compared insomnia severity, sleep characteristics, and co-morbid symptoms, as well as treatment attitudes, adherence, and responses among Black and White participants. RESULTS: Among 156 cancer survivors (28% Black), Black survivors reported poorer sleep quality, longer sleep onset latency, and higher pain at baseline, compared to White survivors (all p < 0.05). Black survivors demonstrated lower adherence to CBT-I than White survivors (61.5% vs. 88.5%, p = 0.006), but their treatment response to CBT-I was similar to white survivors. Black survivors had similar adherence to acupuncture as white survivors (82.3% vs. 93.4%, p = 0.16), but they had greater reduction in insomnia severity with acupuncture (-3.0 points, 95% CI -5.4 to 0.4, p = 0.02). CONCLUSION: This study identified racial differences in sleep characteristics, as well as treatment adherence and responses to CBT-I and acupuncture. To address racial disparities in sleep health, future research should focus on improving CBT-I adherence and confirming the effectiveness of acupuncture in Black cancer survivors.
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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 | 0.003 |
| 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 | 0.000 | 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 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".