A usability study of an internet-delivered behavioural intervention tailored for children with residual insomnia symptoms after obstructive sleep apnea treatment
Bibliographic record
Abstract
Better Nights, Better Days (BNBD) is a 5-session online intervention designed to treat insomnia in 1–10-year-old children (Corkum et al. 2016). Obstructive sleep apnea (OSA) and insomnia commonly occur in children and, after surgical treatment for OSA, it is estimated that up to 50% of children may continue to suffer from insomnia symptoms. Access to insomnia interventions following OSA treatment is limited as there are few programs available, few trained practitioners to deliver these programs, and limited recognition that these problems exist. The current study involved the usability testing of an internet-based parent-directed session of BNBD tailored towards the needs of children (ages 4–10 years) who experience residual insomnia symptoms after treatment of OSA. This new session was added to the BNBD program. Participants (n = 43) included 6 parents, 17 sleep experts, and 20 front-line healthcare providers who completed and provided feedback on the new session. Participants completed a feedback questionnaire, with both quantitative and qualitative questions, after reviewing the session. Quantitative responses analyzed via descriptive statistics suggested that the session was primarily viewed as helpful by most participants, and open-ended qualitative questions analyzed by content analyses generated a mix of positive and constructive feedback. The results provide insights on how to optimally tailor the BNBD program to meet the needs of the target population and suggest that testing the session on a larger scale would be beneficial.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".