Nurse‐led brief sleep education intervention aimed at parents of school‐aged children with neurodevelopmental and mental health disorders: Results from a pilot study
Bibliographic record
Abstract
PURPOSE: To test the effects of a single-session parent sleep educational intervention, led by a pediatric nurse, on sleep in a group of school-aged children attending a National Health Service mental health clinic in Murcia, Spain. DESIGN AND METHODS: Parents/caregivers of 26 children (mean age = 8.58 years; standard deviation = 0.58; 80.8% male) with neurodevelopmental or mental health disorders (84.6% pervasive developmental disorder), and with a suspicion of a behavioral sleep problem, participated in a 45-min group educational session about healthy sleep practices, supplemented by a written handout. The main outcome measures were derived from items of the Pediatric Sleep Questionnaire and included an insomnia composite score, a daytime sleepiness composite score, average sleep onset latency, and sleep duration on both weekdays and weekends. Outcome variables were assessed before and 3 months after the intervention. RESULTS: After the educational intervention, the insomnia and daytime composite scores decreased significantly (ps < 0.03). Weekday and weekend sleep duration significantly increased ( ps < 0.01). No statistically significant difference was observed in sleep onset latency before and after the intervention. PRACTICE IMPLICATIONS: A brief and relatively inexpensive single sleep educational session can positively impact sleep in children with neurodevelopmental and mental health disorders. Thus, this intervention meets the characteristics of a successful "entry level" treatment in a stepped-care approach. The stepped-care model places nurses in a pivotal position to ensure that their patients will receive the least complex and most accessible intervention, from which they are likely to get some benefit, and that a sizeable number of patients who need treatment may receive it. Thus, it is important for nurses in all types of practice settings to have an understanding of healthy sleep patterns, as well as sleep disorders in children. Pediatric nurses, regardless of their setting, are in a unique position to screen children and adolescents for sleep behavior problems or sleep disorders, to educate families about healthy sleep practices, provide guidance and feedback, and recommend referral to pediatric specialized care for more complex assessment and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".