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Record W4391223972 · doi:10.3233/shti231294

Improving Health Equity to Primary Care for First Nations Peoples Living in Northern Queensland

2024· article· en· W4391223972 on OpenAlexaboutno aff
G. Thorn, Janine Cox

Bibliographic record

VenueStudies in health technology and informatics · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)Primary careHealth equityHealth careHealth literacyLiteracyPrimary health careMedicineNursingPopulation healthPopulationGeographyBusinessFamily medicineEnvironmental healthEconomic growthPolitical sciencePublic health

Abstract

fetched live from OpenAlex

With only 13.75% of the identified Aboriginal and Torres Strait Islander population of northern Queensland accessing a billed 715 Health Assessment over a 12-month period, Northern Queensland Primary Health Network (NQPHN) is embarking on an ambitious 12-month program to dramatically improve access to health assessments, and integrated and coordinated care for First Nations' people within its region. By supporting primary care providers with targeted education, training, and digital health literacy bundles and tools, NQPHN aims to facilitate quality and culturally safe 715 Health Assessments to manage chronic conditions. Success of the program will be measured using regional and practice-level data, focusing on the number of Health Assessments and Management Plans for identified chronic conditions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.053
GPT teacher head0.456
Teacher spread0.403 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes1
Has abstractyes

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