Toward Practice Advancement in Emergency Care for Children With Autism Spectrum Disorder
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: There is increasing recognition that children with autism spectrum disorder (ASD) experience challenges in busy clinical environments such as the emergency department (ED). ASD may heighten adverse responses to sensory input or transitions, which can impose greater difficulty for a child to cope with situational demands. These problems can be amplified in the ED because of its busy and unpredictable nature, wait times, and bodily care. There is little literature documenting ED-based needs of children with ASD to inform clinical guidelines. The objective was to identify stakeholder perspectives in determining clinical priorities and recommendations to guide ED service delivery for children with ASD. METHODS: After qualitative interviews with children, parents, and health care providers conducted in a previous phase of this study, focus groups were convened with parents of children with ASD, ED clinicians, and ED administrators (total n = 60). Qualitative data were analyzed based on an interpretive description approach. RESULTS: Participants identified the ED and its delivery of care as insufficient to meet the unique needs of children with ASD. The following clinical priorities were identified: ASD-focused preparedness for ED procedures and processes, wait time management, proactive strategies for sedation and restraint, child-focused support, health care provider capacity building, post-ED follow-up resources, and transition planning to adult care. Heightened child- and family-centered care were strongly recommended.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".