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Record W4400651314 · doi:10.1186/s40900-024-00608-9

Engagement and partnership with consumers and communities in the co-design and conduct of Research: Lessons from the INtravenous iron polymaltose for First Nations Australian patients with high FERRitin levels on haemodialysis (INFERR) clinical trial

2024· letter· en· W4400651314 on OpenAlexaboutno aff
Stephanie Long, Cheryl Ross, J Koops, Katherine Coulthard, Jane Nelson, Archana Khadka Shapkota, Leiana Hewett, Jaclyn Tate-Baker, Jessica Graham, Rose Mukula, Cynthia Tetteh, Libby Hoppo, Cherian Sajiv, Basant Pawar, Heidi Lee Chmielewski, Lorna Murakami Gold, Geetha Rathnayake, Bianca Heron, Teana Brewster-O’Brien, Vijay Karepalli, Louise Maple‐Brown, Robert A. Batey, Peter W. G. Morris, Jane Davies, David Kiran Fernandes, Sajan Thomas, Asanga Abeyaratne, Paul Lawton, Federica Barzi, Sean Taylor, Mark Mayo, Alan Cass, Sandawana William Majoni

Bibliographic record

VenueResearch Involvement and Engagement · 2024
Typeletter
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsGeneral partnershipFerritinMedicineIntravenous ironIron deficiencyCommunity engagementPublic relationsPolitical scienceInternal medicineAnemia

Abstract

fetched live from OpenAlex

BACKGROUND: Engagement and partnership with consumers and communities throughout research processes produces high quality research meeting community needs and promoting translation of research into improved policy and practice. Partnership is critical in research involving Aboriginal and/or Torres Strait Islander people (First Nations Peoples) to ensure cultural safety. We present lessons from the design, implementation and progress of the National Health and Medical Research Council funded INtravenous iron polymaltose for First Nations Australian patients with high FERRitin levels on hemodialysis (INFERR) clinical trial. MAIN BODY: The trial was designed to understand the benefits and harms of iron therapy in First Nations Australians on haemodialysis with anaemia and hyperferritinaemia. The lack of evidence for treatment was discussed with patients who were potential participants. A key element ensuring safe conduct of the INFERR trial was the establishment of the Indigenous Reference Groups (IRGs) comprising of dialysis patients based in the Top End of Australia and Central Australia. Two IRGs were needed based on advice from First Nations communities and researchers/academics on the project regarding local cultural differences and approaches to trial conduct. The IRGs underpin culturally safe trial conduct by providing input into study materials and translating study findings into effective messages and policies for First Nations dialysis patients. Throughout the trial conduct, the IRGs' role has developed to provide key mechanisms for advice and guidance regarding research conduct both in this study and more broadly. Support provided to the IRGs by trial First Nations Research Officers and independent First Nations researchers/academics who simplify research concepts is critical. The IRGs have developed feedback documents and processes to participants, stakeholders, and the renal units. They guarantee culturally safe advice for embedding findings from the trial into clinical practice guidelines ensuring evidence-based approaches in managing anaemia in haemodialysis patients with hyperferritinaemia. CONCLUSION: Active consumer and community partnership is critical in research conduct to ensure research impact. Strong partnership with consumers in the INFERR clinical trial has demonstrated that First Nations Consumers will engage in research they understand, that addresses health priorities for them and where they feel respected, listened to, and empowered to achieve change.

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)0.441
metaresearch head score (Gemma)0.373
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.689

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4410.373
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0020.002
Science and technology studies0.0120.024
Scholarly communication0.0180.022
Open science0.0060.031
Research integrity0.0160.030
Insufficient payload (model declined to judge)0.0070.002

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 head0.937
GPT teacher head0.685
Teacher spread0.252 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations1
Published2024
Admission routes1
Has abstractyes

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