Strong Born—A First of Its Kind National FASD Prevention Campaign in Australia Led by the National Aboriginal Community Controlled Health Organisation (NACCHO) in Collaboration with the Aboriginal Community Controlled Health Organisations (ACCHOs)
Bibliographic record
Abstract
The Strong Born Campaign (2022-2025) was launched by the National Aboriginal Community Controlled Health Organisation (NACCHO) in 2023. Strong Born is the first of its kind national Aboriginal and Torres Strait Islander health promotion campaign to address Fetal Alcohol Spectrum Disorder (FASD) within Australia. Strong Born was developed to address a longstanding, significant gap in health promotion and sector knowledge on FASD, a lifelong disability that can result from alcohol use during pregnancy. Utilizing a strengths-based and culturally sound approach, NACCHO worked closely with the Aboriginal Community Controlled Health Organisations (ACCHOs) to develop the campaign through co-design, as described in this paper. Since its inception, the ACCHOs have continually invested in driving change towards improvements in Aboriginal health determinants and health promotion. The Strong Born Campaign developed culturally safe health promotion tool kits designed for the community and health sector staff and also offered communities the opportunity to apply for FASD Communications and Engagement Grants to engage in local campaign promotion. The tool kits have been disseminated to 92 ACCHOs across Australia. This paper describes the development of the Strong Born Campaign and activities following its launch in February 2023 from an Indigenous context within Australia, as described by NACCHO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".