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Record W2901850239 · doi:10.2196/11471

Accessibility of Primary, Specialist, and Allied Health Services for Aboriginal People Living in Rural and Remote Communities: Protocol for a Mixed-Methods Study

2018· article· en· W2901850239 on OpenAlexvenueno aff
Rona Macniven, Kate Hunter, Michelle Lincoln, Ciaran O’Brien, Thomas Lee Jeffries, Gregory Shein, Alexander J. Saxby, Donna Taylor, Tim Agius, Heather Finlayson, Robyn Martin, Kelvin Kong, Davida Nolan-Isles, Susannah Tobin, Kylie Gwynne

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHealth servicesRural areaProtocol (science)Rural healthPopulationGeographyNursingEnvironmental healthAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Primary, specialist, and allied health services can assist in providing equitable access in rural and remote areas, where higher proportions of Aboriginal and Torres Strait Islander people (Aboriginal Australians) reside, to overcome the high rates of chronic diseases experienced by this population group. Little is currently known about the location and frequency of services and the extent to which providers believe delivery is occurring in a sustained and coordinated manner. OBJECTIVE: The objective of this study will be to determine the availability, accessibility, and level of coordination of a range of community-based health care services to Aboriginal people and identify potential barriers in accessing health care services from the perspectives of the health service providers. METHODS: This mixed-methods study will take place in 3 deidentified communities in New South Wales selected for their high population of Aboriginal people and geographical representation of location type (coastal, rural, and border). The study is designed and will be conducted in collaboration with the communities, Aboriginal Community Controlled Health Services (ACCHSs), and other local health services. Data collection will involve face-to-face and telephone interviews with participants who are health and community professionals and stakeholders. Participants will be recruited through snowball sampling and will answer structured, quantitative questions about the availability and accessibility of primary health care, specialist medical and allied health services and qualitative questions about accessing services. Quantitative data analysis will determine the frequency and accessibility of specific services across each community. Thematic and content analysis will identify issues relating to availability, accessibility, and coordination arising from the qualitative data. We will then combine the quantitative and qualitative data using a health ecosystems approach. RESULTS: We identified 28 stakeholder participants across the ACCHSs for recruitment through snowball sampling (coastal, n=4; rural, n=12; and border, n=12) for data collection. The project was funded in 2017, and enrolment was completed in 2017. Data analysis is currently under way, and the first results are expected to be submitted for publication in 2019. CONCLUSIONS: The study will give an indication of the scope and level of coordination of primary, specialist, and allied health services in rural communities with high Aboriginal populations from the perspectives of service providers from those communities. Identification of factors affecting the availability, accessibility, and coordination of services can assist ways of developing and implementing culturally sensitive service delivery. These findings could inform recommendations for the provision of health services for Aboriginal people in rural and remote settings. The study will also contribute to the broader literature of rural and remote health service provision. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11471.

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)0.101
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.101
Threshold uncertainty score0.533

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1010.044
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0050.004
Science and technology studies0.0080.004
Scholarly communication0.0050.004
Open science0.0060.005
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0590.014

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 head0.161
GPT teacher head0.618
Teacher spread0.457 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations8
Published2018
Admission routes1
Has abstractyes

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