Guided web-based intervention for insomnia targeting breast cancer patients: Feasibility and effect
Bibliographic record
Abstract
Background Insomnia is highly prevalent in breast cancer (BRC) patients, but non-pharmacological treatment is not widely available. The aim of this preliminary study was to investigate whether guided cognitive behavioral therapy via the Internet (I-CBT) is a feasible and effective solution for this undertreated condition in BRC patients, and to investigate who benefits most. Methods An existing evidence based I-CBT sleep intervention (I-Sleep) was adapted for BRC patients. An open mixed methods design was used including qualitative interviews and pre- and post-test questionnaires measuring sleep, fatigue, daily functioning, and psychological distress. Results 100 of the 171 participants (59%) completed the intervention fully and participants highly valued the intervention (7.5 out of 10). Large to small pre-post effect sizes were found on insomnia severity ( d = 1.33) fatigue ( d = 0.24), and daytime functioning ( d = 0.30). Younger patients and patients with more severe insomnia at baseline benefited most from the intervention. Conclusion The I-CBT intervention I-Sleep is feasible, well-accepted, and effective for BRC patients who suffer from insomnia, especially for younger patients and those with more severe insomnia.
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.
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) | 0.006 | 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".