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Record W4294816854 · doi:10.3138/canlivj-2022-0011

Lessons from First Nations partnerships in hepatitis C research and the co-creation of knowledge

2022· article· en· W4294816854 on OpenAlex
Andrew Mendlowitz, Karen E. Bremner, Jordan J. Feld, Lyndia Jones, Evelynne Hill, Elly Antone, Laura Liberty, R. Boucher, Murray Krahn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Liver Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsAssembly of First NationsToronto General HospitalUniversity Health Network
FundersCanadian Institutes of Health ResearchCanada Research ChairsGilead Sciences
KeywordsGeneral partnershipPublic relationsParticipatory action researchCorporate governancePolitical scienceCitizen journalismCapacity buildingCommunity-based participatory researchSociologyMedicineBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Administrative health data provide a rich and powerful tool for health services research. Partnership between researchers and the Ontario First Nations HIV/AIDS Education Circle (OFNHAEC) allowed for comprehensive analyses of the health and economic impacts of hepatitis C virus (HCV) infection in First Nations populations across Ontario, using administrative data. Examples of meaningful involvement of First Nations partners in research using secondary data sources demonstrate how community-based participatory research principles can be adapted to empower First Nations stakeholders and decision-makers. The aim of this review is to summarize and reflect on lessons learned in producing meaningful and actionable First Nations HCV research using health administrative data, from the perspective of health services researchers who collaborated for the first time with First Nations partners. METHODS: We discuss how our relationship with OFNHAEC formed and how engagement contextualized findings and provided opportunities for fostering trust and mutual capacity building. Methods included adherence to data governance principles, agreements outlining ethical conduct, and establishing commitment between partners. RESULTS: Engagement with OFNHAEC enhanced cultural understandings in study conception, design, and analysis, and enabled meaningful lessons for both parties through contextualizing findings together. Partnership ensured attention to factors, such as strength-based approaches and limitations of administrative data in their representation of First Nations peoples, that are not considered in standard HCV health services research using administrative health data. CONCLUSIONS: Collaboration throughout the HCV research provided first-hand experience of the relevance, representation, and importance of incorporating First Nations perspectives in health services research using administrative data.

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.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.204
GPT teacher head0.481
Teacher spread0.277 · 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