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Record W4220746286 · doi:10.1080/24745332.2022.2041328

Chapter 12: An introductory guide to tuberculosis care to improve cultural competence for health care workers and public health professionals serving Indigenous Peoples of Canada

2022· article· en· W4220746286 on OpenAlexaffabout
Jonathan L. Dunn, Marlene Larocque, Deborah Van Dyk, Eduardo Vides, Faiz Ahmad Khan, Tom Wong, Richard Long, Gonzalo G. Alvarez

Bibliographic record

VenueCanadian Journal of Respiratory Critical Care and Sleep Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsOttawa HospitalInstitute of Indigenous Peoples' HealthUniversity of OttawaUniversity of AlbertaPublic Health Agency of CanadaMcGill UniversityAssembly of First NationsMakivik CorporationMétis National CouncilInuit Tapiriit Kanatami
Fundersnot available
KeywordsIndigenousCultural competenceCompetence (human resources)Health careHealth professionalsPublic healthNursingTuberculosisMedicinePolitical scienceSociologyPsychologyPedagogy

Abstract

fetched live from OpenAlex

Health care workers providing services on Indigenous lands and/or working with Indigenous Peoples to do tuberculosis-related work should:educate themselves about the epidemiology of tuberculosis (T...

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.284
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0510.017

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 head0.038
GPT teacher head0.370
Teacher spread0.332 · 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
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

Citations10
Published2022
Admission routes2
Has abstractno

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