Community-based screening and triage connecting First Nations children and youth to local supports: a cross-sectional study
Bibliographic record
Abstract
Background: First Nations children in Canada experience health inequities. We aimed to determine whether a self-report health app identified children’s needs for support earlier in their illness than would typically occur. Methods: Children (aged 8 to 18 yr) were recruited from a rural First Nation community. Children completed the Aaniish Naa Gegii: the Children’s Health and Well-being Measure (ACHWM) and then met with a local mental health worker who determined their risk status. ACHWM Emotional Quadrant Scores (EQS) were compared between 3 groups of children: healthy peers (HP) who were not at risk, those with newly identified needs (NIN) who were at risk and not previously identified, and a typical treatment (TT) group who were at risk and already receiving support. Results: We included 227 children (57.1% girls), and the mean age was 12.9 (standard deviation [SD] 2.9) years. The 134 children in the HP group had a mean EQS of 80.1 (SD 11.25), the 35 children in the NIN group had a mean EQS of 67.2 (SD 13.27) and the 58 children in the TT group had a mean EQS of 66.2 (SD 16.30). The HP group had significantly better EQS than the NIN and TT groups (p < 0.001). The EQS did not differ between the NIN and TT groups (p = 0.8). Interpretation: The ACHWM screening process identified needs for support among 35 children, and the associated triage process connected them to local services; the similarity of EQS in the NIN and TT groups highlights the value of community screening to optimize access to services. Future research will examine the impact of this process over the subsequent year in these groups.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".