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Record W4405856888 · doi:10.1002/acr.25487

“It's Just Good Science”: A Qualitative Study Exploring Equity, Diversity, and Inclusion in Canadian Arthritis Research

2024· article· en· W4405856888 on OpenAlexafffundabout
Megan Thomas, Mark Harrison, Cheryl Barnabé, Charlene Ronquillo, J. Antonio Aviña‐Zubieta, Anna Samson, Michael Kuluva, Natasha Trehan, Mary A. De Vera

Bibliographic record

VenueArthritis Care & Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of OttawaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusCanadian Arthritis Patient AllianceUniversity of CalgaryArthritis Research Centre of CanadaAlberta Bone and Joint Health InstituteCentre for Advancing Health OutcomesSt. Paul's HospitalResearch CanadaUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsInclusion (mineral)Equity (law)Diversity (politics)Health equityQualitative researchSociologyPsychologyMedicineGender studiesPolitical scienceSocial scienceAnthropologyNursingPublic health

Abstract

fetched live from OpenAlex

OBJECTIVE: Despite knowledge that health outcomes vary according to patient characteristics, identity, and geography, including underrepresented populations in arthritis research remains a challenge. We conducted interviews to explore how researchers in arthritis have used equity, diversity, and inclusion (EDI) principles to inform their research. METHODS: Semistructured interviews were conducted with individuals who 1) have experience conducting arthritis research studies, 2) reside in and/or conduct their research in Canada, and 3) speak English or French. Participants were recruited using purposive and respondent-driven sampling. Interviews were conducted over video call and audio recordings were transcribed. Template analysis was applied to interview transcripts to explore participant experiences and perceptions of EDI in arthritis research. RESULTS: Participants (n = 22) identified that a lack of representation in arthritis research translates to the inability to provide comprehensive care. Participants emphasized considering EDI early in all arthritis research to effectively affect a study. Themes were categorized as benefits, barriers, and facilitators. The perceived benefits were the ability to generate knowledge and reduce health disparities. Barriers included mistrust from historically exploited populations, unintended consequences, lack of access to research opportunities, and logistical challenges. Facilitators included building community partnerships, curating diverse research teams, incentivizing researchers and funder support, and fostering humility in research environments. CONCLUSION: Improving representation in research is needed to improve health outcomes for diverse groups of people living with arthritis. Identified barriers to EDI in research must be addressed and partnerships and supports must be facilitated to achieve more representation in arthritis research within Canada.

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)0.037
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0440.023
Scholarly communication0.0080.004
Open science0.0040.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.761
GPT teacher head0.635
Teacher spread0.126 · 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
DomainIncentives
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

Citations2
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueArthritis Care & Research→Same topicMental Health and Patient Involvement→French-language works237,207→