How to provide insomnia interventions to people with cancer: insights from patients
Bibliographic record
Abstract
Chronic insomnia affects approximately one quarter of cancer patients. Non-pharmacologic interventions are the treatment of choice for chronic insomnia, yet they are rarely offered to people with cancer. The study question was how to make these interventions available to cancer patients. Twenty-six cancer patients who had sleep difficulty participated in focus groups or one-to-one interviews. The key questions included: What would be the best way for you to find out about a service for insomnia treatment? What would make it easy/difficult for you to participate? Transcripts were examined independently by three readers who identified participants' answers to the questions, as well as themes that emerged from participants' reflections on their experience with cancer and sleep difficulty. The readers then worked together to reach consensus on a final classification system for describing the content of patients' responses. Participants provided many practical answers to our specific questions. In addition, the following themes emerged: sleep difficulty needs greater recognition by health professionals; patients wish to receive more information about sleep and sleep difficulty; and that although patients perceive sleep as being important, they are reluctant to report sleep problems to doctors. Furthermore, participants recommended that the assessment and treatment of sleep difficulty be integrated into the health care system while considering the cancer-treatment status and energy level of patients.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".