A scoping review of commissioning practices used in the evaluation of Indigenous health and wellbeing programs: Protocol article
Bibliographic record
Abstract
Despite the billions of dollars invested in improving Indigenous health and wellbeing outcomes in Australia, there is little evidence of program effectiveness to inform policy and practice. The deficiency of evaluations is problematic. Critical to this process is the effective engagement of commissioners with Indigenous peoples, which is not well documented. Currently, there is scant evidence on modes of commissioning practices used. This scoping review will aim to identify the spectrum of commissioning practices used when evaluating Indigenous health and wellbeing programs in Australia, codifying them into a model set. Documents (between 2008 and 2020) will be retrieved from Scopus, Proquest, Informit, Google Scholar and via a web-based search that refers to the commissioning of Indigenous health and wellbeing program evaluations in Australia, New Zealand, Canada or the United States. Importantly, the research team is Indigenous-led and the project’s governance, quality and translation framework will be informed by a project advisory group, including Indigenous associates. This will be the first scoping review globally to identify practices used to commission Indigenous health and wellbeing program evaluations. Results will be utilised to strengthen the commissioning practices of Indigenous health and wellbeing programs in Australia and overseas.
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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 | 0.132 | 0.176 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".