Improving the care of children with autism and related neurodevelopmental disorders in emergency department settings: Understanding the knowledge-to-practice continuum of emergency department providers
Bibliographic record
Abstract
Objectives: Emergency department (ED) healthcare providers (HCPs) frequently describe a lack of knowledge in caring for children with autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDD). Our primary objective is to explore gaps in training and clinical exposure reported by ED HCPs caring for children with ASD/NDD. Methods: A two-phase, mixed-methods cross-sectional study was conducted. In phase 1, an interprofessional sample of tertiary care paediatric ED HCPs (physicians, nurses, social workers, and child life specialists) were surveyed about their experiences and perceived gaps in managing children with ASD/NDD. These responses informed phase 2, where six semi-structured interviews were conducted. Interview transcripts were analyzed to determine themes around the discomfort of ED HCPs caring for children with ASD/NDD. Results: The majority, 54/78 (69%) of eligible staff completed the survey. A minority (42.5%) of HCPs had mandatory training on ASD/NDD, and 80% would value continuing education. Some ED HCPs (41.2%) had previous personal or professional experiences with children with ASD/NDD that facilitated deeper empathy and awareness of system challenges. Interviews revealed four themes of ED HCP discomfort with this population: 1) added considerations of interacting with children and families with ASD/NDD; 2) the ED as a single touch point in complex and limited healthcare systems; 3) recognizing comfort in discomfort; and 4) the need to implement practical interventions to improve care. Conclusions: ED HCPs are motivated to improve care for children with ASD/NDD. Alongside broader systems interventions, future educational interventions can narrow ED HCP gaps identified through this work.
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 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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".