Creating healing-informed, strengths-based and culturally responsive services: Transforming health professional practice through the Australian fetal alcohol spectrum disorder (FASD) Indigenous Framework
Bibliographic record
Abstract
Purpose Health professionals play a central role in facilitating access to essential prenatal alcohol exposure (PAE) and Fetal Alcohol Spectrum Disorder (FASD) services. However, deficit-oriented narratives around PAE and FASD can undermine the capacity of health professionals to provide appropriate and meaningful knowledge and support, leading to unrecognised and unsupported complex developmental, health, and social and emotional wellbeing needs. For Aboriginal peoples, these unmet needs are compounded by the intergenerational impacts of colonisation, racism and disability, perpetuating cycles of inequity, injustice and harm. This study explored what non-Aboriginal health professionals need to know, be and do to deliver healing-informed, strengths-based and culturally responsive PAE and FASD knowledge and support to Aboriginal peoples. The findings were interpreted through the Australian FASD Indigenous Framework, to deepen understanding and guide practice transformation. Methods Grounded in the Indigenous methodologies of Dadirri (deep listening from a place of still awareness) and yarning, 20 Aboriginal and non-Aboriginal knowledge holders connected to the outreach team at Carbal Medical Service, who generously shared their knowledge, perspectives and experiences. Collaborative yarning and narrative analysis informed the collective story and its underpinning storylines. Main findings The study highlighted that rebuilding trust, fractured by colonisation, is central to equitable healthcare. Key shifts include understanding the enduring impacts of colonisation, unlearning unconscious biases and colonial dynamics, and adopting healing-informed, strengths-based and culturally responsive approaches that prioritise connection, deep listening, self-determination and walking alongside Aboriginal peoples. Principal conclusions In the context of PAE and FASD, this study revealed how Western practices have broken the trust of Aboriginal peoples and how current systems continue to maintain this harm. It also offers a powerful story of hope, showing how embedding Aboriginal ways of knowing, being and doing can transform health professional practice and create strengths-based, healing-informed and culturally responsive services that benefit all Australians.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| 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".