Stakeholder perspectives on managing the adolescent sleep crisis using a transdiagnostic self-management app for sleep disturbances: A qualitative follow-up study
Bibliographic record
Abstract
Sleep problems are diverse and pervasive among the adolescent population. Current sleep health interventions are ill-equipped to address the sleep crisis. We developed DOZE ( D elivering O nline Z zz’s with E mpirical Support), which is a self-management evidence-based app for sleep disturbances. In an initial study, we found that DOZE was perceived as an acceptable and effective support for teen sleep. In a qualitative follow-up study, we engaged with students and other stakeholders to understand their experiences with implementing, disseminating, and using DOZE. The study employed a combination of qualitative surveys and semi-structured interviews to students ( n = 21) and stakeholders (teachers, social workers, and researchers; n = 5), respectively. Reflexive thematic analysis was used to identify themes related to experiences implementing and engaging with the app. Students reported increased sleep regularity and sleep duration after using DOZE. Facilitators included greater integration of the app with school curriculum, timing of implementation, and researcher involvement in supporting knowledge dissemination and engagement. Barriers included worries about phone use at night and normalized poor sleep patterns among adolescents. There is need to identify ways to support implementation and engagement in different communities. Researchers continue to engage with stakeholders to support timely access to sleep health interventions for adolescents.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".