ABCs of SLEEPING Tool: Improving Access to Care for Pediatric Insomnia
Bibliographic record
Abstract
Objective: Pediatric insomnia is common among children; however, access to evidence-based treatment is limited. The ABCs of SLEEPING tool was developed to facilitate assessment of children’s sleep habits and provide evidence-based recommendations. The title serves as a mnemonic device that captures practice areas known to be foundational to healthy sleep: A ge-appropriate B edtimes and wake-times with C onsistency, S chedules and routines, L ocation, E xercise and diet, no E lectronics in bedroom or before bed, P ositivity, I ndependence, and N eeds met, equal G reat sleep. The tool includes an online questionnaire for parents, followed by electronic feedback highlighting problem areas and handouts including evidence-based recommendations. The current study assesses the usability of the ABCs of SLEEPING tool. Method: Twenty-two parents used the ABCs of SLEEPING tool, then provided feedback. Eight health care providers also reviewed the tool and provided feedback. Results: The ABCs of SLEEPING tool was rated as highly usable, desirable, accessible, and credible. Participants noted that minor modifications, such as prioritizing and customizing recommendations, may enhance the usefulness and valuableness. Participant feedback will be incorporated into a modified version. Conclusion: The ABCs of SLEEPING tool has the potential to address the need for a comprehensive and accessible resource for parents and to support health care providers in assessing and treating pediatric insomnia, and thus improving access to care. Implications for Impact Statement This study describes the development and usability testing of a tool designed to increase access to information and resources focused on healthy sleep practices for children. Feedback from parents and health care providers suggest that the ABCs of SLEEPING tool has the potential to support initial assessment and intervention of childhood behavioral sleep problems.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".