Training, knowledge, attitudes and practices of Canadian health care providers regarding sleep and sleep disorders in children
Bibliographic record
Abstract
BACKGROUND: Sleep disorders are prevalent in children and are associated with significant comorbidity. OBJECTIVE: To assess the training, knowledge, attitudes and practices of Canadian health care providers (HCPs) regarding sleep disorders in children. METHOD: A 42-item survey, designed to collect information on frequency of paediatric sleep disorders-related screening and diagnosis, implementation of evidence-based interventions and related knowledge base, was completed by HCPs. RESULTS: Ninety-seven HCPs completed the survey. One per cent obtained training in paediatric sleep during undergraduate training and 3% obtained such training during their residencies, yet 34.9% estimated that 25 to 50% of their patients suffered from sleep disorders. Most HCPs thought that sleep disorders significantly impacted children's health and daytime function. Most HCPs screened for developmental sleep issues, but not consistently for sleep disorders. Most recommended evidence-based behavioural interventions for behavioural sleep disorders, but some also reported behavioural interventions that were not first-line or recommended. Inadequate knowledge regarding melatonin use was evident. Most participants reported rarely/never ordering a sleep study for a child with suspected obstructive sleep apnea (OSA). Most were familiar with surgical and weight loss management options for OSA; many were unfamiliar with benefits of continuous positive airway pressure. Participants' knowledge scores were highest on developmental and behavioural aspects of sleep, and lowest on sleep disorders. CONCLUSIONS: HCPs exhibit significant gaps in their knowledge, screening, evaluation and treatment practices for paediatric sleep disorders. Training at the undergraduate, graduate and postgraduate levels, as well as Continuing Medical Education are needed to optimize recognition, treatment and follow-up of paediatric sleep disorders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".