Evidence about art-based interventions for Indigenous people: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Indigenous people experience a unique set of health inequalities and social determinants that can negatively affect their physical health, mental health and wellness. This critical state of affairs is compounded by the limited availability of culturally appropriate care services and treatments for the different groups. In response, increasing numbers of studies are turning their focus to art-based interventions and how these might benefit Indigenous lives. The proposed scoping review aims to map this growing field of research. METHODS AND ANALYSIS: . Academic databases and grey literature sources will be searched to identify appropriate studies for inclusion. The search strategies of all databases were tested on 25 April 2024. This will be followed by a two-step screening process to be conducted by two researchers and consisting of (1) a title and abstract review and (2) a full-text review. Data from the selected studies will be extracted, collated and charted to summarise all relevant interventions, their outcomes and key findings. An Indigenous research partner will be hired as a consultant, and the research will be further informed by other stakeholders. ETHICS AND DISSEMINATION: This study is the first step in a research programme involving working with Indigenous artists to codesign a pilot art-based intervention aimed at improving mental health and wellness among Indigenous people. The scoping review will identify the specific components in documented art-based interventions that have proven beneficial to this group. Since it will draw exclusively on data from published and public sources, no ethics approval is required. The results will be disseminated through knowledge translation activities with Indigenous organisations and art therapy groups; a summary of the results will also be distributed through Indigenous networks.
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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 | 0.124 | 0.097 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.015 |
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".