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Record W4205975915 · doi:10.1111/imj.15293

IMPROVING COVID‐19 RESPONSES FOR PRIORITY COMMUNITIES USING FIRST NATIONS HEALTH PRINCIPLES

2021· article· en· W4205975915 on OpenAlexaboutno aff
Gerry Afphm, Ngaree Blow, Edwina Dorney, Kate Cheney, Kirsten Black, Luke E. Grzeskowiak, Kevin McGeechan

Bibliographic record

VenueInternal Medicine Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
Fundersnot available
KeywordsUnit (ring theory)Public healthMedicineGeneral partnershipCommunity engagementPublic relationsCommunity healthPandemicNursingCoronavirus disease 2019 (COVID-19)Political sciencePsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.205
GPT teacher head0.469
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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