IMPROVING COVID‐19 RESPONSES FOR PRIORITY COMMUNITIES USING FIRST NATIONS HEALTH PRINCIPLES
Bibliographic record
Abstract
Background: The Aboriginal and Torres Strait Islander community have been very successful in preparing and responding to the COVID-19 pandemic. There is scope for First Nations health principles to provide a more effective pandemic response for other priority communities in Victoria, including refugees and culturally and linguistically diverse (CALD) communities. Objectives: We aimed to establish a unique model of Case, Contact and Outbreak Management (CCOM) dedicated to priority communities, based on Aboriginal community-controlled health principles and selfdetermination. Methodology: A new model was developed and implemented within the existing CCOM structure in the Department of Health and Human Services (DHHS). This model was developed in consultation with the Aboriginal health unit and the COVID-19 CALD taskforce. Key components of this model included a dedicated CCOM team, community liaisons, advisory oversight, crisis brokerage for COVID-19 positive cases, and cultural safety training for all staff. Results: Implementation of this new CCOM model has seen new processes of local community engagement and partnership, cultural safety training across the CCOM teams, recruitment of identified Aboriginal and bicultural public health officers and an overall cultural shift within the CCOM unit.
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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 | 0.037 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".