72 Validating MyHEARTSMAP, an emergency psychosocial self-assessment and management tool, among youth with minor traumatic brain injuries or minor orthopaedic injuries seen in the Paediatric Emergency Department
Bibliographic record
Abstract
Abstract Background Fewer than 20% of the estimated 1.2 million Canadian youths living with mental health (MH) concerns receive adequate care. Paediatric emergency department (PED) visits related to MH are increasing across North America. The online self-assessment tool, MyHEARTSMAP, was developed to facilitate screening of MH concerns in the PED and general practice. MyHEARTSMAP assesses 10 psychosocial areas, mapping to four domains of MH (Psychiatry, Function, Social, and Youth Health) to provide domain-specific recommendations for patient management (Figure A). Objectives We evaluated the convergent validity of MyHEARTSMAP when compared to established psychosocial self-assessment tools: Paediatric Quality of Life (PedsQL) and Strengths and Difficulties Questionnaire (SDQ). Design/Methods We conducted a cross-sectional study among youths and parents enrolled in a larger cohort study: Advancing Concussion Assessment in Paediatrics (A-CAP). Participants were children aged 8 to 16 years old with mild traumatic brain injury or orthopaedic injury and their parents. Participants were recruited from two PEDs in Alberta and British Columbia and were asked to complete MyHEARTSMAP, in addition to the PedsQL and SDQ completed in their A-CAP study procedures. We evaluated three MH domains from MyHEARTSMAP (PSYCHIATRY FUNCTION, AND SOCIAL) to their corresponding score sections in PedsQL (EMOTIONAL, SCHOOL, and SOCIAL) and SDQ (EMOTIONAL, none, and CONDUCT and PEER). We calculated Pearson correlation coefficients between these corresponding domains and sections. Results We recruited 40 child and parent pairs from Alberta and 82 from BC. The children were on average aged 12.6 years old (SD 2.2) and 44% were female. The tools screened participants as “at-risk” for various MH concerns at a rate of 26.7% to 60.8% for MyHEARTSMAP, 2.5% to 13.9% for PedsQL, and 12.3% to 16.0% for SDQ. Overall, MyHEARTSMAP was moderately correlated with PedsQL (mean ±95% CI: r = 0.405±0.151) and SDQ (mean ±95% CI: r = 0.322±0.162). Correlations (±95% CI) by MyHEARTSMAP domain for the child and parent versions, respectively, were as follows: PSYCHIATRY PedsQL (r = 0.483±0.140 / 0.509±0.134) and SDQ (r = 0.417±0.150 / 0.598±0.116); FUNCTION PedsQL (r = 0.578±0.122 / 0.455±0.143); SOCIAL PedsQL (r = 0.249±0.170 / 0.158±0.175) and SDQ (r = 0.207±0.172 / 0.067±0.178). Conclusion In conclusion, MyHEARTSMAP PSYCHIATRY and FUNCTION domains have moderate convergent validity to PedsQL and SDQ. Unlike PedsQL and SDQ, the evaluation of social issues in MyHEARTSMAP is MH-specific, resulting in low convergent validity for the SOCIAL domain.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".