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Characterization of indigenous community engagement in arthritis studies conducted in Canada, United States of America, Australia and New Zealand

2018· review· en· W2902135766 on OpenAlexafffundabout
Chu Yang Lin, Adalberto Loyola‐Sánchez, Kelle Hurd, Elizabeth D. Ferucci, Louise Crane, Bonnie Healy, Cheryl Barnabé

Bibliographic record

VenueSeminars in Arthritis and Rheumatism · 2018
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsAlberta Health ServicesAssembly of First NationsUniversity nuhelot'ine thaiyots'i nistameyimâkanak Blue QuillsCanadian Arthritis Patient AllianceUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineIndigenousArthritisFamily medicineImmunologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Research adhering to community engagement processes leads to improved outcomes. The level of Indigenous communities' engagement in rheumatology research is unknown. OBJECTIVE: To characterize the frequency and level of community engagement reporting in arthritis studies conducted in Australia (AUS), Canada (CAN), New Zealand (NZ) and the United States of America (USA). METHODS: Studies identified through systematic reviews on topics of arthritis epidemiology, disease phenotypes and outcomes, health service utilization and mortality in Indigenous populations of AUS, CAN, NZ and USA, were evaluated for their descriptions of community engagement. The level of community engagement during inception, data collection and results interpretation/dissemination stages of research was evaluated using a custom-made instrument, which ranked studies along the community engagement spectrum (i.e. inform-consult-involve-collaborate-empower). Meaningful community engagement was defined as involving, collaborating or empowering communities. Descriptive analyses for community engagement were performed and secondary non-parametric inferential analyses were conducted to evaluate the possible associations between year of publication, origin of the research idea, publication type and region of study; and meaningful community engagement. RESULTS: Only 34% (n = 69) of the 205 studies identified reported community engagement at ≥ 1 stage of research. Nearly all studies that engaged communities (99% (n = 68)) did so during data collection, while only 10% (n = 7) did so at the inception of research and 16% (n = 11) described community engagement at the results' interpretation/dissemination stage. Most studies provided community engagement descriptions that were assessed to be at the lower end of the spectrum. At the inception of research stage, 3 studies reported consulting communities, while 42 studies reported community consultation at data collection stage and 4 studies reported informing or consulting communities at the interpretation/dissemination of results stage. Only 4 studies described meaningful community engagement through all stages of the research. Inferential statistics identified that studies with research ideas that originated from the Indigenous communities involved were significantly more associated with achieving meaningful community engagement. CONCLUSIONS: The reporting of Indigenous community engagement in published arthritis studies is limited in frequency and is most frequently described at the lower end of the community engagement spectrum. Processes that support meaningful community engagement are to be promoted.

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)0.083
metaresearch head score (Gemma)0.185
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.185
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0130.015
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0010.001
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 head0.076
GPT teacher head0.335
Teacher spread0.260 · 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.

Study designQualitative
DomainMethods
GenreReview

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
Published2018
Admission routes3
Has abstractyes

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