Applying strengths-based approaches to nutrition research and interventions in Indigenous Australian communities
Bibliographic record
Abstract
This paper provides a background to strengths-based approaches used in health and considers what these have to offer in the context of public health nutrition, with particular reference to work with Australian Aboriginal and Torres Strait Islander peoples.Deficit, disease and dysfunction permeate approaches in health fields, including nutrition. Public health has focused on gathering evidence about ‘what works’ from this deficit perspective, particularly in those communities identified as vulnerable. Strengths-based approaches, on the other hand, work with the assets already existing in individuals, communities and institutions to support the conditions for health. Although strengths-based approaches are used in some health fields, they are under-utilised in public health nutrition. A strengths-based paradigm draws on the theory of salutogenesis to accentuate positive capacities so that nutrition professionals and clients/communities can jointly identify problems and activate solutions.Research processes and findings from a number of participatory Indigenous nutrition health projects will be discussed. This research has identified significant social resources within Australian Indigenous communities and these assets offer points from which to work.A strengths-based paradigm offers a different language with which to address nutrition inequalities. It can contribute to empowering Indigenous individuals and communities towards healthier nutrition. We propose that redressing the current imbalance between strengths and deficit-based approaches is needed in public health nutrition and consider the nature and potentials of strengths-based approaches in nutrition, with particular reference to their use in Aboriginal and Torres Strait Islander 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 | 0.117 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".