Validation of Hearts-Map, A Psychosocial Assessment Tool Applied to Children and Youth with Mental Health-Related Paediatric Emergency Visits
Bibliographic record
Abstract
Abstract BACKGROUND: Mental health-related pediatric emergency department (PED) visits are increasing annually, and there is a need for a validated comprehensive standardized assessment tool to better manage these patients. OBJECTIVES: The primary objective of this study was to evaluate HEARTSS-MAP, a psychosocial assessment tool, in terms of inter-rater agreement among clinicians as well as the tool’s performance in predicting needs for acute psychiatric consultation for hospitalization. DESIGN/METHODS: The HEARTSS-MAP evaluation was done in two phases. We retrospectively reviewed 101 cases randomly sampled from a cohort of patients who sought care at the BC Children’s Hospital (BCCH) PED for mental health complaints. Narratives pertaining to each patient’s psychosocial assessment were recorded. Clinicians, including two emergency physicians, a bedside nurse, a nurse practitioner, and two psychiatrists, were blinded to the patients' outcomes, and individually applied the HEARTSS-MAP tool to the clinical narratives. The inter-rater agreement was calculated using Cohen’s kappa statistics. We then evaluated the tool’s sensitivity and specificity in predicting admission for the retrospective cohort, as well as a prospective cohort of 62 patients assessed and managed by a PED clinician using the HEARTSS-MAP. RESULTS: There was substantial agreement between the two pediatric emergency reviewers (κ=0.7), and moderate agreement between the pedi-atric emergency physicians and the nurse practitioner (κ=0.6), and the bedside nurse (κ=0.5). Pediatric psychiatrists had fair agreement between themselves (κ=0.3), and between psychiatrists and emergency physicians (κ=0.4). Based on retrospective data, HEARTSS-MAP had a sensitivity of 91% (95%CI: 71 to 99%), and a specificity of 41% (95%CI: 30 to 53%). When applied to prospectively collected data, the sensitivity was 100% (95%CI: 75 to 100%), and specificity was 33% (95%CI: 20 to 48%). CONCLUSION: HEARTSS-MAP, the first standardized psychosocial assessment tool to be implemented at BCCH PED, demonstrates strong inter-rater reliability between emergency clinicians, with a high sensitivity in identifying patients with mental health complaints requiring hospital admission.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".