Reliability of <scp>HEARTSMAP</scp> as a Tool for Evaluating Psychosocial Assessment Documentation Practices in Emergency Departments for Pediatric Mental Health Complaints
Bibliographic record
Abstract
OBJECTIVES: The goal of this study was to assess the reliability of HEARTSMAP as a standardized tool for evaluating the quality of psychosocial assessment documentation of pediatric mental health (MH) presentations to the emergency department (ED). In addition, we report on current documentation practices. METHODS: We conducted a retrospective cross-sectional study of pediatric (up to age 17) MH-related visits to four EDs between April 1, 2013, and March 31, 2014. The primary outcome was the inter-rater agreement when evaluating the completeness of pediatric emergency psychosocial assessments using the HEARTSMAP tool. The secondary outcome was to describe the adequacy of documentation of emergency pediatric MH assessments, using HEARTSMAP as a guide for a complete assessment. RESULTS: A total of 400 medical records (100 from each site) were reviewed. We observed near-perfect inter-rater agreement (κ = 0.99-1.00) regarding the presence of documentation and good-to-perfect agreement (κ = 0.71-1.00) regarding whether sufficient information was documented to score a severity level for every component of an emergency psychosocial assessment. Inter-rater agreement regarding whether referrals or resources were documented for identified needs was observed to be good to very good (κ = 0.62-0.98). Current psychosocial documentation practices were found to be inconsistent with significant variability in the presence of documentation pertaining to HEARTSMAP sections between medical centers and initial clinician assessor and whether specialized MH services were involved prior to discharge. CONCLUSIONS: The HEARTSMAP tool can be reliably used to assess pediatric psychosocial assessment documentation across a diverse range of EDs. Current documentation practices are variable and often inadequate, and the HEARTSMAP tool can aid in quality improvement initiatives to standardize and optimize care for the growing burden of pediatric mental illness.
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 | 0.050 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".