MétaCan
Menu
Back to cohort
Record W4368367939 · doi:10.3389/frsle.2023.1158983

Optimizing the Better Nights, Better Days for Children with Neurodevelopmental Disorders program for large scale implementation

2023· article· en· W4368367939 on OpenAlexafffund
Alzena Ilie, Matt Orr, Shelly K. Weiss, Isabel M. Smith, Graham J. Reid, Ana Hanlon‐Dearman, Cary A. Brown, Evelyn Constantin, Roger Godbout, Sarah Shea, O. Ipsiroglu, Penny Corkum

Bibliographic record

VenueFrontiers in Sleep · 2023
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of British ColumbiaMcGill University Health CentreUniversité de MontréalUniversity of AlbertaWestern UniversityDalhousie UniversityUniversity of TorontoChildren’s Health Research InstituteMontreal Children's HospitalUniversity of ManitobaIzaak Walton Killam Health Centre
FundersChildren's Health FoundationKids Brain Health Network
KeywordsRandomized controlled trialMotivational interviewingIntervention (counseling)PsychologyClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Objective Pediatric insomnia is one of the most commonly reported disorders, especially in children with neurodevelopmental disorders. Better Nights, Better Days for Children with Neurodevelopmental Disorders ( BNBD-NDD ) is a transdiagnostic, self-guided, eHealth behavioral sleep intervention developed for parents of children with NDDs ages 4–12 years with insomnia. After usability testing, a randomized controlled trial (RCT) was conducted to evaluate the effectiveness of the BNBD-NDD program. By interviewing RCT participants after their outcome measures were collected, we sought to determine the barriers and facilitators that affect the reach, effectiveness, adoption, implementation, and maintenance of the BNBD-NDD intervention, as well as to assess whether barriers and facilitators differ across levels of engagement with the program and NDD groups. Method Twenty parents who had been randomized to the treatment condition of the RCT participated in this study. These parents participated in virtual semi-structured qualitative interviews about their experiences with the BNBD-NDD program. Rapid analysis was used, in which one researcher facilitated the interview, and another simultaneously coded the interview using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Results Overall, more facilitators than barriers were identified for Reach, Effectiveness, Implementation, and Maintenance, whereas for Adoption more barriers emerged. Participants who were engaged reported more facilitators about the BNBD-NDD program design and behavior change, while unengaged participants mentioned needing more support to help facilitate their use of the program. Lastly, parents of children with ASD reported more facilitators and more barriers than did parents of children with ADHD. Conclusion With this feedback from participants, we can optimize BNBD-NDD for large-scale implementation, by modifying the program to better support parents, helping them implement the strategies effectively at home, and increasing the accessibility of this evidence-based treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.014
GPT teacher head0.300
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in SleepSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207