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Record W4296449922 · doi:10.3390/ijerph191811693

Indigenous and Non-Indigenous Theories of Wellbeing and Their Suitability for Wellbeing Policy

2022· article· en· W4296449922 on OpenAlexaboutno aff
Tamara Mackean, Madison Shakespeare, M. Fisher

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersFlinders FoundationUniversity of South AustraliaFlinders University
KeywordsIndigenousTypologyEquity theorySociologyPublic policyPublic relationsPolitical scienceEconomic growthEconomic JusticeEcologyEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.447
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2022
Admission routes1
Has abstractyes

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