Preventing Drift through Continued Co-Design with a First Nations Community: Refining the Prototype of a Tiered FASD Assessment
Bibliographic record
Abstract
As part of the broader Yapatjarrathati project, 47 remote health providers and community members attended a two-day workshop presenting a prototype of a culturally-safe, tiered neurodevelopmental assessment that can identify fetal alcohol spectrum disorder (FASD) in primary healthcare. The workshop provided a forum for broad community feedback on the tiered assessment process, which was initially co-designed with a smaller number of key First Nations community stakeholders. Improvement in self-reported attendee knowledge, confidence, and perceived competence in the neurodevelopmental assessment process was found post-workshop, assessed through self-report questionnaires. Narrative analysis described attendee experiences and learnings (extracted from the workshop transcript), and workshop facilitator experiences and learnings (extracted from self-reflections). Narrative analysis of the workshop transcript highlighted a collective sense of compassion for those who use alcohol to cope with intergenerational trauma, but exhaustion at the cyclical nature of FASD. There was a strong desire for a shared responsibility for First Nations children and families and a more prominent role for Aboriginal Health Workers in the assessment process. Narrative analysis from workshop facilitator reflections highlighted learnings about community expertise, the inadvertent application of dominant cultural approaches throughout facilitation, and that greater emphasis on the First Nation’s worldview and connection to the community was important for the assessment process to be maintained long-term. This study emphasised the benefit of continued co-design to ensure health implementation strategies match the needs of the community.
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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 | 0.057 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".