Patient- and Family-Centered Care in the Emergency Department for Children With Autism
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) care processes and environments impose unique challenges for children with autism spectrum disorder (ASD). The implementation of patient- and family-centered care (PFCC) emerges as a priority for optimizing ED care. In this article, as part of a larger study, we explore PFCC in the context of ASD. Our aims were to examine how elements of PFCC were experienced and applied relative to ED care for children with ASD. METHODS: Qualitative interviews were conducted with parents and ED service providers, drawing on a grounded theory approach. Interviews were audio recorded, transcribed verbatim, and analyzed by using established constant comparison methods. Data were reviewed to appraise the reported presence or absence of PFCC components. RESULTS: Fifty-three stakeholders (31 parents of children with ASD and 22 ED service providers) participated in interviews. Results revealed the value of PFCC in autism-based ED care. Helpful attributes of care were a person-centered approach, staff knowledge about ASD, consultation with parents, and a child-focused environment. Conversely, a lack of staff knowledge and/or experience in ASD, inattention to parent expertise, insufficient communication, insufficient family orientation to the ED, an inaccessible environment, insufficient support, a lack of resources, and system rigidities were identified to impede the experience of care. CONCLUSIONS: Findings amplify PFCC as integral to effectively serving children with ASD and their families in the ED. Resources that specifically nurture PFCC emerge as practice and program priorities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".