Indigenous and Non-Indigenous Theories of Wellbeing and Their Suitability for Wellbeing Policy
Bibliographic record
Abstract
A growing interest among governments in policies to promote wellbeing has the potential to revive a social view of health promotion. However, success may depend on the way governments define wellbeing and conceptualize ways to promote it. We analyze theories of wellbeing to discern twelve types of wellbeing theory and assess the suitability of each type of theory as a basis for effective wellbeing policies. We used Durie's methodology of working at the interface between knowledge systems and Indigenous dialogic methods of yarning and deep listening. We analyzed selected literature on non-Indigenous theories and Indigenous theories from Australia, New Zealand, Canada and the United States to develop a typology of wellbeing theories. We applied political science perspectives on theories of change in public policy to assess the suitability of each type of theory to inform wellbeing policies. We found that some theory types define wellbeing purely as a property of individuals, whilst others define it in terms of social or environmental conditions. Each approach has weaknesses regarding the theory of change in wellbeing policy. Indigenous relational theories transcend an 'individual or environment' dichotomy, providing for pluralistic approaches to health promotion. A broad theoretic approach to wellbeing policy, encompassing individual, social, equity-based and environmental perspectives, is recommended.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".