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Record W2115884540 · doi:10.1177/1049732310385824

Access to Primary Care From the Perspective of Aboriginal Patients at an Urban Emergency Department

2010· article· en· W2115884540 on OpenAlexafffundabout
Annette J. Browne, Victoria Smye, Patricia Rodney, S. Tang, Bill Mussell, John O’Neil

Bibliographic record

VenueQualitative Health Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsSimon Fraser UniversityUniversity of TorontoNative Mental Health Association of CanadaUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsEmergency departmentPovertyEthnographyPrimary careRacializationDisadvantageQualitative researchParticipant observationPerspective (graphical)MedicineDisadvantagedNursingSociologyFamily medicinePolitical scienceRace (biology)Gender studies

Abstract

fetched live from OpenAlex

In this article, we discuss findings from an ethnographic study in which we explored experiences of access to primary care services from the perspective of Aboriginal people seeking care at an emergency department (ED) located in a large Canadian city. Data were collected over 20 months of immersion in the ED, and included participant observation and in-depth interviews with 44 patients triaged as stable and nonurgent, most of whom were living in poverty and residing in the inner city. Three themes in the findings are discussed: (a) anticipating providers' assumptions; (b) seeking help for chronic pain; and (c) use of the ED as a reflection of social suffering. Implications of these findings are discussed in relation to the role of the ED as well as the broader primary care sector in responding to the needs of patients affected by poverty, racialization, and other forms of disadvantage.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.006
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.168
GPT teacher head0.586
Teacher spread0.418 · 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 designQualitative
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

Citations223
Published2010
Admission routes3
Has abstractyes

Explore more

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