Short Report: Barriers and facilitators to parents' implementation of a transdiagnostic eHealth sleep intervention for children with neurodevelopmental disorders
Bibliographic record
Abstract
Background: Insomnia is highly prevalent in children diagnosed with neurodevelopmental disorders (NDDs) and has negative effects on physical and mental health and wellbeing. Lack of evidence-based intervention programs and barriers to treatment (e.g., time/cost) reduce treatment access. To address these problems, the possibility was explored of modifying the Better Nights, Better Days intervention for typically developing (TD) children (BNBD-TD) to make it appropriate for children with NDD. Aims: The current study's aim was to examine qualitative data from exit interviews conducted during a usability study. Parents of children with NDD used BNBD-TD and reported on barriers and facilitators experienced while implementing the intervention. Methods/procedures: Participants were 15 Canadian parents of children aged 4 to 10 years who were formally diagnosed with an NDD. Parents implemented the BNBD-TD intervention with their children and participated in a semi-structured exit interview to provide perspectives on their user experience. Results: Based on an inductive thematic analysis, key facilitators included increased self-efficacy, positive outcomes for the family (e.g., improved sleep problems, parent validation), improved sleep related beliefs/attitudes, and increased motivation. Key barriers included time challenges, struggles when trying to improve sleep problems, and psychosocial factors with negative effects on implementation (e.g., burnout, stress, and/or exhaustion). Conclusions/implications: Barriers and facilitators identified resulted in recommendations to include more program supports, including helping parents to plan for implementation success.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".