Can-SOLVE CKD: Capturing Our IDEA Journey as a Patient-Oriented Kidney Research Network
Bibliographic record
Abstract
Background: Despite increasing recognition that Inclusion, Diversity, Equity, and Accessibility (IDEA) principles are essential to research, how to meaningfully quantify and apply these principles at a network level is unclear. Here, we outline a holistic approach taken by our patient-oriented kidney research network, Can-SOLVE CKD. Methods: A multidisciplinary team, composed of patient partners, researchers, clinicians, and network partners, co-developed a series of 7 brainstorming workshops (3 groups; 19 patient partners, 17 researchers/clinicians, 13 staff from July 2023 to January 2024) to identify the network’s existing IDEA strengths and key priorities. A thematic analysis identified themes to inform the network’s IDEA mobilization plan. Results: Four themes were identified: (1) Strength in existing network culture through Indigenous input and patient-oriented approach, establishing culturally safe spaces and ensuring systematic support and safety in communication; (2) Barriers in equitable participation due to a fragmented understanding of opportunities and accommodation limitations; (3) Ensuring permanence within the broader health context via maintenance concerns, awareness of network initiatives and promoting kidney health equity; and, (4) Outreach at the public, network and team levels focused on overcoming recruitment barriers and enhancing impact. This reveals an opportunity to tailor implementation strategies based on the scale of change within the network, ensuring that IDEA interventions are appropriately calibrated to the magnitude of the mobilization plan. Conclusion: Meaningful assessment and application of IDEA principles requires the involvement of people with diverse lived experiences. We describe a collaborative, holistic approach our patient-oriented kidney research network has taken to identify core strengths and guide our IDEA strategy moving forward. Funding: Government Support – Non-U.S.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".