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Record W2787561828 · doi:10.18584/iipj.2018.9.1.1

Interplay Wellbeing Framework: Community Perspectives on Working Together for Effective Service Delivery in Remote Aboriginal Communities

2018· article· en· W2787561828 on OpenAlexvenueno aff
Eva McRae‐Williams, Jessica Yamaguchi, Byron Wilson, Rosalie Schultz, Tammy Abbott, Sheree Cairney

Bibliographic record

VenueInternational Indigenous Policy Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersCharles Darwin UniversityFlinders UniversityAustralian Government
KeywordsService delivery frameworkEmpowermentIndigenousSociologyCorporate governancePublic relationsService (business)Space (punctuation)Work (physics)Community engagementPolitical scienceBusinessEngineeringMarketingComputer scienceEcology

Abstract

fetched live from OpenAlex

Access to effective services and programs is necessary to improve wellbeing for Aboriginal and Torres Strait Islander people living in remote Australia. Without genuine participation of Aboriginal community members in the design, governance, and delivery of services, desired service delivery outcomes are rarely achieved. Using a "shared space" model, Aboriginal communities, governments, and scientists came together to design and develop the Interplay Wellbeing Framework. This Framework brings together stories and numbers (or qualitative and quantitative data) to represent community values for the purpose of informing program and policy agendas. This article unpacks what community members saw as making a service work well and why. The domains of empowerment and community functioning are discussed and their relationship to effective service delivery demonstrated.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0130.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.027
GPT teacher head0.400
Teacher spread0.373 · 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 designQualitative
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

Citations7
Published2018
Admission routes1
Has abstractyes

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