Predictors of emergency department use by adolescents and adults with autism spectrum disorder: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To determine predictors of emergency department (ED) visits in a cohort of adolescents and adults with autism spectrum disorder (ASD). DESIGN: Prospective cohort study. SETTING: Community-based study from Ontario, Canada. PARTICIPANTS: Parents reported on their adult sons and daughters with ASD living in the community (n=284). MAIN OUTCOME MEASURES: ED visits for any reason, ED visits for medical reasons and ED visits for psychiatric reasons over 1 year. RESULTS: Among individuals with ASD, those with ED visits for any reason were reported to have greater family distress at baseline (p<0.01), a history of visiting the ED during the year prior (p<0.01) and experienced two or more negative life events at baseline (p<0.05) as compared with those who did not visit the ED. Unique predictors of medical versus psychiatric ED visits emerged. Low neighbourhood income (p<0.01) and living in a rural neighbourhood (p<0.05) were associated with medical but not psychiatric ED visits, whereas a history of aggression (p<0.05) as well as being from an immigrant family (p<0.05) predicted psychiatric but not medical emergencies. CONCLUSIONS: A combination of individual and contextual variables may be important for targeting preventative community-based supports for individuals with ASD and their families. In particular, attention should be paid to how caregiver supports, integrative crisis planning and community-based services may assist in preventing or minimising ED use for this vulnerable population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".