1031 Exploring the Impact of Cognitive Behavioral Therapy for Insomnia (CBT-I) on Daytime Productivity in Survivors of Breast Cancer
Bibliographic record
Abstract
Abstract Introduction Post-treatment insomnia disorder and fatigue symptoms can impair work and daytime productivity in breast cancer survivors. Cognitive Behavioral Therapy for Insomnia (CBT-I) significantly improves insomnia and daytime fatigue. This feasibility study examined whether improving insomnia and fatigue using CBT-I is associated with improved work and activity productivity in breast cancer survivors. Methods 10 survivors of early stage breast cancer participated in 7 weekly individual CBT-I sessions. The primary outcome was the Work Productivity and Activity Impairment Questionnaire-General Health (WPAIQ-GH) questionnaire. Secondary outcomes were the Insomnia Severity Index (ISI) and the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF). Assessments were conducted at baseline and post-treatment. Paired samples t-tests examined the impact of CBT-I on productivity and fatigue. Linear regression assessed whether change in fatigue was associated with change in productivity. Results Participants had a mean age of 50.8 (range 42-63) and the majority were diagnosed with stage II (60%) cancer. There was a significant reduction in fatigue [t(9)= 2.43, p =.04] and activity impairment due to insomnia [t(9)= 3.105, p <.05] following treatment. Insomnia affected 52% of work productivity at baseline with a non-significant decrease to 15% following treatment [t(3)= 2.25 p= .110]. Reductions in fatigue were significantly associated with reductions in activity impairment [F(1,8)= 7.25, p =.03], accounting for 47.5% of the variability. Conclusion Treating insomnia with CBT-I significantly improved daytime productivity, activity impairment, and fatigue. Controlled research with larger sample sizes is warranted to confirm these preliminary results. Support Nyissa Walsh is a trainee in the Cancer Research Training Program of the Beatrice Hunter Cancer Research Institute (BHCRI). Dr. Sheila Garland is supported by a Scotiabank New Investigator Award from BHCRI.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".