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Record W2125168009 · doi:10.1177/1049732309360538

Urban Aboriginal Understandings and Experiences of Tuberculosis in Montreal, Quebec, Canada

2010· article· en· W2125168009 on OpenAlexafffundabout
Mary Ellen Macdonald, Nicole Rigillo, Paul Brassard

Bibliographic record

VenueQualitative Health Research · 2010
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsThematic analysisTuberculosisExperiential knowledgeDiseaseMedicineQualitative researchExperiential learningSilenceGerontologyFamily medicinePsychologySociologySocial sciencePathology

Abstract

fetched live from OpenAlex

Tuberculosis (TB) continues to be a serious health issue for some Aboriginal populations in Canada. In this article we build on two previous studies partnered with an Aboriginal community organization to examine TB in Montreal, Quebec. The current study draws on qualitative interviews with Aboriginal participants who had either themselves contracted TB or knew someone who had the disease. Thematic analysis revealed a high level of experiential knowledge among participants, although gaps in biomedical knowledge about the disease and available resources were apparent even among those who had been ill. Negative memories and experiences relating to the colonial history of TB treatment emerged as significant for many participants, helping to explain a silence around the topic of TB in the community. Barriers to TB testing were identified, including the fear of positive test results, the burden of long-term treatment for either latent or active disease, and systemic barriers within and mistrust of the health system. Community support for, as well as negative reactions toward, individuals ill with TB were identified. We conclude with a number of community health lessons to address the treatment and prevention of TB in urban populations.

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)0.004
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 score0.074
Threshold uncertainty score0.537

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0300.011
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.151
GPT teacher head0.528
Teacher spread0.377 · 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

Citations19
Published2010
Admission routes3
Has abstractyes

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