MyHEARTSMAP: development and evaluation of a psychosocial self-assessment tool, for and by youth
Bibliographic record
Abstract
BACKGROUND: Paediatric mental health-related visits to the emergency department are rising. However, few tools exist to identify concerns early and connect youth with appropriate mental healthcare. Our objective was to develop a digital youth psychosocial assessment and management tool (MyHEARTSMAP) and evaluate its inter-rater reliability when self-administered by a community-based sample of youth and parents. METHODS: We conducted a multiphasic, multimethod study. In phase 1, focus group sessions were used to inform tool development, through an iterative modification process. In phase 2, a cross-sectional study was conducted in two rounds of evaluation, where participants used MyHEARTSMAP to assess 25 fictional cases. RESULTS: MyHEARTSMAP displays good face and content validity, as supported by feedback from phase 1 focus groups with youth and parents (n=38). Among phase 2 participants (n=30), the tool showed moderate to excellent agreement across all psychosocial sections (κ=0.76-0.98). CONCLUSIONS: Our findings show that MyHEARTSMAP is an approachable and interpretable psychosocial assessment and management tool that can be reliably applied by a diverse community sample of youth and parents.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".