AYA ‘Can-Sleep’ Program : protocol for a stepped-care, cognitive behavioral therapy based approach to the management of sleep difficulties in adolescents and young adults with cancer
Bibliographic record
Abstract
Abstract Background Sleep problems are reported in up to 50% of adolescents and young adults (AYA) with cancer. Cognitive Behaviour Therapy for insomnia (CBTi) is considered the gold-standard treatment. In the AYA population, CBTi is associated with improvements in insomnia, daytime sleepiness, fatigue and quality of life. In adults, stepped care interventions can improve accessibility to CBTi. This study aims to evaluate the acceptability and feasibility of a stepped care CBTi program in AYA with cancer. Methods and analysis : AYA (target N=80) will be screened using the Insomnia Severity Index (ISI) and Epworth Sleepiness Scale (ESS). When sleep difficulties are identified by the ISI and/or ESS, they will be screened for obstructive sleep apnoea and restless leg syndrome and referred as indicated. The remainder will be offered a stepped care sleep program including CBT self-management and coaching (first step). Participants will then be re-screened at 5 weeks, and those with ongoing sleep difficulties will be offered individualised CBT (second step). Recruitment and retention rates, adherence to intervention, and time taken to deliver screening and intervention will be collected to assess the feasibility of the program. AYA and clinicians will complete evaluation surveys to assess the acceptability of the AYA Can-Sleep Program. Discussion We seek to contribute to the evidence-base regarding screening and treatment of sleep difficulties in the AYA population by implementing the AYA Can-Sleep program and determining its feasibility and acceptability as an approach to care in an Adolescent & Young Adult Cancer Service.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.012 |
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".