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Record W2899279365 · doi:10.1186/s12913-018-3578-8

National Survey of Indigenous primary healthcare capacity and delivery models in Canada: the TransFORmation of IndiGEnous PrimAry HEAlthcare delivery (FORGE AHEAD) community profile survey

2018· article· en· W2899279365 on OpenAlexafffundabout
Jordan W. Tompkins, Selam Mequanint, Douglas E. Barre, Meghan Fournie, Michael Green, Anthony J. Hanley, Mariam Naqshbandi Hayward, Merrick Zwarenstein, Stewart B. Harris

Bibliographic record

VenueBMC Health Services Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsPublic Health OntarioUniversity of TorontoQueen's UniversityCape Breton UniversityCentre for Family MedicineWestern University
FundersAstraZenecaHealth CanadaCape Breton UniversityLawson FoundationCanadian Institutes of Health ResearchAstraZeneca Canada
KeywordsIndigenousHealth careHealth administrationNursing researchHealth informaticsCommunity healthMedicineCommunity engagementPublic healthNursingFamily medicineEnvironmental healthPublic relationsPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: There is a significant deficiency of national health information for Indigenous peoples in Canada. This manuscript describes the Community Profile Survey (CPS), a community-based, national-level survey designed to identify and describe existing healthcare delivery, funding models, and diabetes specific infrastructure and programs in Indigenous communities. METHODS: The CPS was developed collaboratively through FORGE AHEAD and the First Nations and Inuit Health Branch of Health Canada. Regional and federal engagement and partnerships were built with Indigenous organizations to establish regionally-tailored distribution of the 8-page CPS to 440 First Nations communities. Results were collected (one survey per community) and reported in strata by region, with descriptive analyses performed on all variables. Results were shared with participating communities and regional/federal partners through tailored reports. RESULTS: A total of 84 communities completed the survey (19% response rate). The majority of communities had a health centre/office to provide service to their patients with diabetes, with limited on-reserve hospitals for ambulatory or case-sensitive conditions. Few healthcare specialists were located on-site, with patients frequently travelling off-site (> 40 km) for diabetes-related complications. The majority of healthcare professionals on-site were Health Directors, Community Health Nurses, and Home Care Nurses. Many communities had a diabetes registry but few reported a diabetes surveillance system. Regional variation in healthcare services, diabetes programs, and funding models were noted, with most communities engaging in some type of innovative strategy to improve care for patients with diabetes. CONCLUSIONS: The CPS is the first community-based, national-level survey of its kind in Canada. Although the response rate was low, the CPS was distributed and successfully administered across a broad range of First Nations communities, and future considerations would benefit from a governance structure and leadership that strengthens community engagement, and a longitudinal research approach to increase the representativeness of the data. This type of information is important for communities and regions to inform decision making (maintain successes, and identify areas for improvement), strengthen health service delivery and infrastructure, increase accessibility to healthcare personnel, and allocate funding and/or resources to build capacity and foster a proactive chronic disease prevention and management approach for Indigenous communities across Canada. TRIAL REGISTRATION: Current ClinicalTrial.gov protocol ID NCT02234973 . Registered: September 9, 2014.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.976
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.140
GPT teacher head0.380
Teacher spread0.240 · 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

Citations13
Published2018
Admission routes3
Has abstractyes

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