Social determinants of Aboriginal and Torres Strait islander social and emotional wellbeing
Bibliographic record
Abstract
This chapter explores current understandings of the determinants of Aboriginal and Torres Strait Islander social and emotional wellbeing (SEWB) and its development. We show that the determinants of this wellbeing are multiple, interconnected, and develop and act across the life course from conception to late life, all of which influence the expression of positive or negative wellbeing. This chapter discusses those determinants that prompt, facilitate or constrain social and emotional wellbeing in all individuals. It then discusses a range of risk factors identified by Aboriginal and Torres Strait Islander peoples as specifically influencing or determining the development and expression of their wellbeing. Importantly, the chapter focuses on those determinants, or combination of determinants, that increase the likelihood of poor outcomes for Aboriginal and Torres Strait Islander people—the risk factors as well as those that promote or protect positive wellbeing, and the unique protective factors contained within Indigenous cultures and communities that serve as sources of strength and resilience. Finally, it shows that the risk and protective factors impacting on the wellbeing of Aboriginal and Torres Strait Islander people differ in important ways from mainstream concepts of ‘mental health’ and the experiences of other 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".