Program Report: Can-SOLVE CKD Network Presents an Inclusive Method for Developing Patient-Oriented Research Tools
Bibliographic record
Abstract
PURPOSE OF PROGRAM: Given the growing interest in patient-oriented research (POR) initiatives, there is a need to provide relevant training and education on how to engage with patients as partners on research teams. SOURCES OF INFORMATION: As part of its mandate to develop appropriate training materials, the patient-oriented renal research network, Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD), established a training and Mentorship Committee (TMC). METHODS: The committee brings together a unique combination of Indigenous and non-Indigenous patient partners (including caregivers, family members, and living donors), researchers, as well as patient engagement and knowledge translation experts, combining a multitude of perspectives and expertise. Following an assessment of training needs within the network, the TMC undertook the co-development of 5 learning modules to address the identified gaps. Subsequently, the committee divided into working groups tasked with developing content using a consultive and iterative approach informed by the DoTTI framework for building web-based tools for patients. In addition, the TMC embodied the guiding principles of inclusiveness, support, mutual respect, and co-building as set out by the Patient Engagement Framework through the Strategy for Patient-Oriented Research (SPOR) of the Canadian Institutes of Health Research. KEY FINDINGS: The 5 new modules include: A Patient Engagement Toolkit, Storytelling for Impact, Promoting Kidney Research in Canada (KidneyPRO), Wabishki Bizhiko Skaanj Learning Pathway, and Knowledge Translation. The TMC's approach to developing these modules demonstrates how a diverse group of stakeholders working together can create tools to support high-quality POR. This also provides a roadmap for other health research entities interested in developing similar tools within their unique domains. LIMITATIONS: The landscape of patient engagement in research is constantly evolving. This underscores the need for sustained resources to keep POR tools and training relevant and up-to-date. Sustaining such resources may not be feasible for all research entities. IMPLICATIONS: Collaborative approaches integrating patients in the development of POR tools ensure the content is relevant and meaningful to patients. Broader adoption of such approaches has great potential to address existing gaps and enhance the Canadian POR landscape.
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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.076 | 0.025 |
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".