A Delphi study and development of a social and emotional wellbeing screening tool for Australian First Nations Peoples living in the Torres Strait and Northern Peninsula Area of Australia
Bibliographic record
Abstract
Tools screening depression and anxiety developed using the Western biomedical paradigm are still used with First Nations Peoples globally, despite calls for cross-cultural adaption. Recent work by this research team found that tools used to screen for depression and anxiety were inappropriate for use with Australian First Nations Peoples living in the Torres Strait and Northern Peninsula Area of Australia. The objective of this Delphi study, the second phase of a broader four-phase project, was to gain consensus from an expert mental health and/or social and emotional wellbeing (SEWB) panel to inform the development of an appropriate screening tool. This Delphi study took place between March and May 2023. Three sequential rounds of anonymous online surveys delivered using QualtricsTM were planned, although only two were needed to reach 75% consensus. The first round sought consensus on whether a new screening tool needed to be developed or whether existing tools could be used. The second round achieved consensus. Twenty-eight experts (47% response rate) participated across the two Delphi rounds. In the second round, 83% of these experts agreed or strongly agreed that a new screening tool, using the holistic First Nations concept of social and emotional wellbeing, be developed. Ninety-four percent of them agreed that it should take a Yarning approach. These findings enabled the development of a new SEWB screening tool that adopted a Yarning (narrative) approach designed for use in primary care and geriatric settings in the region. The new tool has four different Yarning areas: Community engagement and behaviour; Stress worries; Risk; and Feeling strong. Guidelines for tool use are integrated as well as Summary and Recommendation sections. At a macro-level this project responds to the need for new screening tools that are underpinned by First Nations worldviews.
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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 | 0.121 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".