Does emergency department use and post-visit physician care cluster geographically and temporally for adolescents who self-harm? A population-based 9-year retrospective cohort study from Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Clustering of adolescent self-harming behaviours in the context of health care utilization has not been studied. We identified geographic areas with higher numbers of adolescents who (1) presented to an emergency department (ED) for self-harm, and (2) were without a physician follow-up visit for mental health within 14 days post-ED visit. METHODS: We extracted a population-based cohort of adolescents aged 15-17 years (n = 3,927) with ED visits during 2002-2011 in Alberta, Canada. We defined the case as an individual with one or more ED presentations for self-harm in the fiscal year of the analysis. Crude case rates were calculated and clusters were identified using a spatial scan. RESULTS: The rates decreased over time for ED visits for self-harm (differences: girls -199.6/100,000; p < 0.01; boys -58.8/100,000; p < 0.01), and for adolescents without a follow-up visit within 14 days following an ED visit for self-harm (differences: girls -108.3/100,000; p < 0.01; boys -61.9/100,000; p < 0.01). Two space-time clusters were identified: (1) a North zone cluster during 2002-2006 (p < 0.01) and (2) a South zone cluster during 2003-2007 (p < 0.01). These clusters had higher numbers of adolescents who presented to the ED for self-harm (relative risks [RRs]: 1.58 for cluster 1, 3.54 for cluster 2) and were without a 14-day physician follow-up (RRs: 1.78 for cluster 1, 4.17 for cluster 2). In 2010/2011, clusters in the North, Edmonton, and Central zones were identified for adolescents with and without a follow-up visit within 14 days following an ED visit for self-harm (p < 0.01). CONCLUSIONS: The rates for ED visits for adolescents who self-harm and rates of adolescents without a 14-day physician follow-up visit following emergency care for self-harm decreased during the study period. The space-time clusters identified the areas and years where visits to the ED by adolescents for self-harm were statistically higher than expected. These clusters can be used to identify locations where adolescents are potentially not receiving follow-up and the mental health support needed after emergency-based care. The 2010/2011 geographic cluster suggests that the northern part of the province still has elevated numbers of adolescents visiting the ED for self-harm. Prospective research is needed to determine outcomes associated with adolescents who receive physician follow-up following ED-based care for self-harm compared to those who do not.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".