Insomnia among cancer caregivers: A proposal for tailored cognitive behavioral therapy.
Bibliographic record
Abstract
Caregivers are relatives, friends, or partners who have a significant relationship with and provide assistance (i.e., physical, emotional) to a patient with often life-threatening, serious illnesses. Between 40 and 76 percent of caregivers for people with cancer experience sleep disturbance. This is thought to be due, in part, to the unique responsibilities, stressors, and compensatory behaviors endemic to caregiving that serve as precipitating and perpetuating factors of insomnia. Sleep disturbances are associated with significant alterations in one's mental and physical health. Once chronic, insomnia does not remit naturally. Cognitive-behavioral therapy for insomnia (CBT-I) is well-suited to address the multifaceted contributing factors unique to caregivers' sleep disturbance, yet only one intervention has tested a CBT-I informed intervention among cancer caregivers. Toward the goal of developing effective, tailored treatments for insomnia in caregivers, we address the distinct presentation of insomnia among cancer caregivers and describe key modifications to standard CBT-I that address these specific needs and enhance sensitivity and feasibility, modeled in a demonstrative case vignette. Future research must seek to provide a wide range of effective treatment options for this population, including internet-based, dyadic, and alternative integrative medicine treatments. Applicability of key modifications for caregivers of patients with other chronic illnesses is discussed. Establishing empirically-supported interventions for insomnia among cancer caregivers has the potential to enhance their quality of life and care provided, lead to improved bereavement outcomes, and attenuate the notable mental and physical health disparities present in this vulnerable population.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".