Improving patient-centred care in the emergency department: Implementation of a Sensory Toolkit for children with autism
Bibliographic record
Abstract
Emergency department (ED) visits for children with autism can present challenges due to the unique sensory needs of this population. This Quality Improvement (QI) project executed two Plan-Do-Study-Act (PDSA) cycles to create and implement a Sensory Toolkit in the ED for children with autism. Most caregivers (94%; n = 31/33) and healthcare providers (HCPs; 86%; n = 37/44) identified the need for sensory items in the ED. In PDSA Cycle 1, 100% of caregivers (n = 21) and HCPs (n = 3) agreed/strongly agreed that the ED Sensory Toolkit was helpful. In PDSA Cycle 2, 92% of caregivers (n = 12/13) and 100% of HCPs (n = 3) agreed/strongly agreed that they were helpful. The Sensory Toolkit was positively evaluated by caregivers of children with autism and HCPs during the child's visit to the ED. There is an opportunity to adapt the Sensory Toolkit for other EDs and areas of the hospital.
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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| 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".