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Record W2802140333 · doi:10.5206/uwomj.v86i2.1407

Indigenous access barriers to health care services in London, Ontario

2017· article· en· W2802140333 on OpenAlexvenueaboutno aff
Stephanie McConkey

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousMainstreamHealth careFocus groupMedicineNursingHealth equityPublic healthPolitical scienceSociology

Abstract

fetched live from OpenAlex

Introduction: Indigenous peoples in Canada suffer higher rates of health inequalities and encounter a number of health services access barriers when compared to their non-Indigenous counterparts. Indigenous peoples experience social and economic challenges, cultural barriers, and discrimination when accessing mainstream health services. Methods: In London, Ontario, 21 interviews and 2 focus groups (n = 25) with service providers were completed, each session spanning approximately 1 to 1.5 hours. Interviews were voice recorded and transcribed verbatim. Themes were identified using NVIVO 10 software. Findings: Approximately 2 to 5% of clients are Indigenous in hospital-based services. There are a number of social factors that influence whether Indigenous peoples access health services. Indigenous peoples do not have access to adequate pain medications because physicians are reluctant to provide Indigenous patients with pain medications due to common perceptions of addiction. Indigenous peoples also have barriers accessing a family physician because physicians are reluctant to take on new patients with complex health needs. Conclusion: Systemic discrimination is still alive in the health care system; therefore, there is a need for cultural safety training among physicians to increase awareness of access barriersand challenges that many Indigenous patients face when seeking health care.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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 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

Citations17
Published2017
Admission routes2
Has abstractyes

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