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Record W1982830467 · doi:10.1080/15402002.2013.874348

Core Competencies for Health Professionals' Training in Pediatric Behavioral Sleep Care: A Delphi Study

2014· article· en· W1982830467 on OpenAlexafffund
Katelynn E. Boerner, Janie Coulombe, Penny Corkum

Bibliographic record

VenueBehavioral Sleep Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsCore competencyDelphi methodSleep (system call)Health professionalsHealth carePsychologyMedicineIdentification (biology)Medical educationNursingComputer science

Abstract

fetched live from OpenAlex

The need to train non-sleep-specialist health professionals in evidence-based pediatric behavioral sleep care is well established. The objective of the present study was to develop a list of core competencies for training health professionals in assisting families of 1- to 10-year old children with behavioral insomnia of childhood. A modified Delphi methodology was employed, involving iterative rounds of surveys that were administered to 46 experts to obtain consensus on a core competency list. The final list captured areas relevant to the identification and treatment of pediatric behavioral sleep problems. This work has the potential to contribute to the development of training materials to prepare non-sleep-specialist health professionals to identify and treat pediatric behavioral sleep problems, ideally within stepped-care frameworks.

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)0.050
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.164
GPT teacher head0.435
Teacher spread0.271 · 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 designQualitative
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

Citations27
Published2014
Admission routes2
Has abstractyes

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