Developing Measures of Quality for the Emergency Department Management of Pediatric Suicide-Related Behaviors
Bibliographic record
Abstract
OBJECTIVE: Given the public health importance of suicide-related behaviors and the corresponding gap in the performance measurement literature, we sought to identify key candidate process indicators (quality of care measures) and structural measures (organizational resources and attributes) important for emergency department (ED) management of pediatric suicide-related behaviors. METHODS: We reviewed nationally endorsed guidelines and published research to establish an inventory of measures. Next, we surveyed expert pediatric ED clinicians to assess the level of agreement on the relevance (to patient care) and variability (across hospitals) of 42 candidate process indicators and whether 10 hospital and regional structural measures might impact these processes. RESULTS: Twenty-three clinicians from 14 pediatric tertiary-care hospitals responded (93% of hospitals contacted). Candidate process indicators identified as both most relevant to patient care (≥87% agreed or strongly agreed) and most variable across hospitals (≥78% agreed or strongly agreed) were wait time for medical assessment; referral to crisis intervention worker/program; mental health, psychosocial, or risk assessment requested; any inpatient admission; psychiatric inpatient admission; postdischarge treatment plan; wait time for first follow-up appointment; follow-up obtained; and type of follow-up obtained. Key hospital and regional structural measures (≥87% agreed or strongly agreed) were specialist staffing and type of specialist staffing in or available to the ED; regional policies, protocols, or procedures; and inpatient psychiatric services. CONCLUSIONS: This study highlighted candidate performance measures for the ED management of pediatric suicide-related behaviors. The 9 candidate process indicators (covering triage, assessment, admission, discharge, and follow-up) and 4 hospital and regional structural measures merit further development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".