Community-Based screening and triage versus standard referral of Aboriginal children
Bibliographic record
Abstract
Health solutions for Aboriginal children should be guided by their community and grounded in evidence. This manuscript presents a prospective cohort study protocol, designed by a community-university collaborative research team. The study’s goal is to determine whether community-based screening and triage lead to earlier identification of children’s emotional health needs, and to improved emotional health 1 year later, compared to the standard referral process. We are recruiting a community-based sample and a clinical sample of children (ages 8 to 18 years) within one Canadian First Nation. All participants will complete the Aboriginal Children’s Health and Well-being Measure (ACHWM)© and a brief triage assessment with a local mental health worker. All participants will be followed for 1 year. Children with newly identified health concerns will be immediately connected to local services, generating a new opportunity to improve health. The development of the research design and its execution were impacted by several events (e.g., disparate worldviews, loss of access to schools). This manuscript describes lessons learned that are important to guide future community-based research with First Nations people. The optimal research design in an Aboriginal context is one that responds directly to local decision makers’ needs and respectfully integrates Aboriginal ways of knowing with Western scientific principles. Such an approach is critical because it will generate meaningful results that will be rapidly adopted, thus reducing the knowledge-to-action gap.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".