How Australian First Nations peoples living in the Torres Strait and Northern Peninsula Area of Australia describe and discuss social and emotional well-being: a qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: Colonisation has, and continues to, negatively impact the mental well-being of Australia's First Nations peoples. However, the true magnitude of the impact is not known, partially because clinicians have low levels of confidence in using many existing screening tools with First Nations clients. In addition, many authors have critiqued the use of tools designed for Western populations with First Nations peoples, because their worldview of health and well-being is different. Therefore, the aim of the overarching study is to develop an appropriate mental well-being screening tool(s) for older adults (aged 45 and over) living in the Torres Strait that can be used across primary health and geriatric settings. This protocol describes the first phase designed to achieve the overarching aim-yarning about social and emotional well-being (inclusive of mental well-being) in First Nations peoples living in the Torres Strait and Northern Peninsula Area of Australia. METHOD AND ANALYSIS: The study will be guided by decolonising and participatory action research methodologies. Yarning is an Australian First Nations relational method that relies on storytelling as a way of sharing knowledge. Yarning circles will be conducted with community members and health and aged care workers living on six different island communities of the Torres Strait. Participants will be recruited using purposive sampling. Thematic analysis of the data will be led by Torres Strait Islander members of the research team. ETHICS AND DISSEMINATION: The Far North Queensland, Human Research Ethics Committee (HREC) (HREC/2021/QCH/73 638-1518) and James Cook University HREC (H8606) have approved this study. Dissemination of study findings will be led by Torres Strait members of the research team through conferences and peer-reviewed publications.
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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 | 0.038 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".