Developing an appropriate depression and anxiety screening tool for use with Australian First Nations peoples living in the Torres Strait and Northern Peninsula Area of Australia: Protocol for a Delphi study
Bibliographic record
Abstract
Tools that screen for 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 the research team found that tools used to screen for depression and anxiety were not appropriate for use with Australian First Nations peoples living in the Torres Strait and Northern Peninsula Area (NPA). of Australia. Consequently, the objective of this Delphi study is to gain consensus from an expert mental health panel to inform the development of an appropriate depression and anxiety screening tool(s). A Delphi study with Australian expert panellists will be used to reach consensus about whether an existing screening tool should be used or whether adaption or new tool development should take place. Three sequential rounds of anonymous online surveys will be used to reach consensus. The first round will seek consensus about the tool(s). Subsequent rounds will seek consensus on the development of the tool(s) identified in round one. Panellists will be identified using a combination of authorship of related publications, established national clinical or research profile in First Nations mental health, and/or by peer referral. Consensus will be reached when 75% of the panel agree. When agreement is not reached suggestions will be taken to the next round. If agreement is not achieved by the third round, the Steering Committee will make any outstanding decisions. Dissemination of the findings through continuing community engagement, conference presentations and publications will be led by Torres Strait Islander members of the research team.
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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 | 0.085 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".