How to provide insomnia interventions to people with cancer: insights from patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it