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Record W4415615744 · doi:10.1016/j.fnhli.2025.100089

Commissioning stronger evaluations of Indigenous health and wellbeing programs: A scoping review of government and non-government Indigenous evaluation commissioning practices

2025· article· en· W4415615744 on OpenAlexaboutno aff
Summer May Finlay, Jenni Judd, James A. Smith, H. Blair Simpson, Bronwyn Fredericks, Amohia Boulton, Yvette Roe, Janaya Pender, Sophie Kerrigan, Anna Temby, Melissa J. Opozda, Margaret Cargo

Bibliographic record

VenueFirst Nations Health and Wellbeing - The Lowitja Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersNational Health and Medical Research Council
KeywordsProject commissioningIndigenousAotearoaGovernment (linguistics)PublishingBest practiceHealth care

Abstract

fetched live from OpenAlex

• Indigenous-led, delegative and co-design models of commissioning Indigenous health and wellbeing evaluation commissioning models are good practice. • Embedding of Indigenous values early is fundamental to good evaluation commissioning. • Poor commissioning practices negatively impact evaluation outcomes. • Commissioner cultural capability is vital to good practice commissioning principles. • Substantive Indigenous engagement across the Commissioning process is imperative. Calls for evidence-based Indigenous health and wellbeing policy and practice development have increased over the last few decades. Funded evaluations of health and wellbeing programs provide a vital contribution to generating the evidence required to support these aims. The utility of program evaluations in contributing to this evidence base is often adversely affected by the processes surrounding their commissioning. Despite the importance of commissioning processes to the efficacy of Indigenous health and wellbeing evaluations, to date there are no developed models to support and guide good practice. This scoping review systematically searched literature from Australia, Canada, Aotearoa New Zealand and the USA to identify government and non-government agencies commissioning practices of Indigenous health and wellbeing program evaluations. Through this review, commissioning practices were characterised according to Indigenous good practice principles, and gaps in current knowledge in these practices were identified. This review identified three key themes in commissioning practice: power, cultural safety and reciprocity. Through consideration of these principles, five models of current commissioning practice were defined: Indigenous-led, delegatory, co-design, participatory, and top down. The models represent a spectrum of Indigenous engagement, involvement and decision-making in the commissioning process, with Indigenous-led, delegatory and co-design being considered as good practice models, with higher levels of self-determination, power, respect, reciprocity and cultural safety. The defining of good practice models of evaluation commissioning provides a vital starting point to assist commissioners, evaluators and service providers in undertaking Indigenous health and wellbeing evaluations that both benefit Indigenous communities and provide much needed evidence to inform policy and commissioning practices. The defining of these models allows for further research into their practical application and opens possibilities for the development of guidelines to support better commissioning practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.149
metaresearch head score (Gemma)0.321
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.851
Threshold uncertainty score0.786

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1490.321
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0220.026
Science and technology studies0.0040.005
Scholarly communication0.0090.009
Open science0.0040.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.396
Teacher spread0.366 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainEvaluation
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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