Feasibility of a self‐help treatment for insomnia comorbid with cancer
Bibliographic record
Abstract
OBJECTIVE: Professionally administered cognitive-behavioral therapy (CBT) is efficacious for treating insomnia in breast cancer survivors. However, given the limited accessibility to CBT for insomnia in cancer clinics, there is a need to develop other delivery formats for this intervention. This feasibility study examined patients' satisfaction with a self-help CBT for insomnia comorbid with cancer and gathered some preliminary data on its effect on sleep and associated features. METHODS: Eleven breast cancer patients reporting insomnia symptoms received a 6-week intervention composed of a 60-min video using an animated cartoon format and 6 short booklets developed in French. Patients completed a semi-structured interview at post-treatment, a battery of self-report scales and a daily sleep diary (14 days) at pre- and post-treatment and at a 3-month follow-up. RESULTS: At post-treatment interview, comments about the treatment material were uniformly positive. On a questionnaire, patients also reported to be satisfied with the treatment overall, with the video and the booklets' content, as well as with their sleep improvement at post-treatment (scores from 2.7 to 3.1; scale from 0 to 4). From pre- to post-treatment, moderate-to-large effect sizes and statistically and clinically significant differences were found on most sleep variables, as well as overall quality of life. These therapeutic gains were well sustained at a 3-month follow-up. CONCLUSIONS: Patients were satisfied with the treatment received. Although it is too early to draw any firm conclusion, treatment outcomes suggest a potential for integration into routine cancer care as a first-line sleep management intervention. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".