The KRESCENT 2.0 Health Research Training Platform Application Process: Program Report
Bibliographic record
Abstract
Purpose of Program: The Kidney Research Scientist Core Education and National Training Program (KRESCENT) was launched in 2005 to enhance kidney research capacity in Canada and foster knowledge translation across the 4 pillars of health research. This program report describes the pan-Canadian KRESCENT 2.0 Health Research Training Platform (HRTP) application process that was awarded a 5-year grant through the pilot Canadian Institutes of Health Research (CIHR) HRTP program, ensuring continuation of this capacity-building program in Canada. Sources of Information: Grant application documents including meeting minutes, break out group summaries and recommendations, and Gantt timeline charts. Other resources included websites and journal articles. Methods: All application-related documents were reviewed. Clarification of process and timelines was provided through interviews with the Nominated Principal Applicant (NPA) Dr R. Todd Alexander, Principal Applicants (PAs) Drs Adeera Levin and Sunny Hartwig, Project Manager (PM) Dr Jenn Klein, members of the Patient Community Advisory Network (PCAN), and the Kidney Foundation of Canada Program (KFoC) Manager Ms. Julie Wysocki via in-person and virtual meetings as well as email correspondence. Key Findings: The KRESCENT 2.0 HRTP application represents a 6-month pan-Canadian effort spearheaded by the NPA and a pan-Canadian team of PAs spanning multiple jurisdictions, disciplines, and sectors. Early engagement of stakeholders in the Canadian kidney research community, outstanding PM administrative support from the onset of the application process were identified as pivotal for the success of the application. Other essential factors for success included graphic design assistance to effectively communicate key and complex concepts, appointment of an EDI champion, engagement with a diverse group of collaborators, and strategic collaboration with other HRTP grant applicants to navigate the ambiguities of the pilot HRTP call. Indispensable, scrupulous final review of the complete application package was generously provided by Dr Robert Quinn (University of Alberta) prior to final grant submission to CIHR. Limitations: Unlike other funded HRTP applicants, KRESCENT is an established kidney training platform for a small cohort of trainees. Our results may not generalize well to HRTPs with large group cohorts or newly established HRTPs. Implications: This program report may provide valuable guidance for other groups seeking to successfully navigate the CIHR HRTP application process.
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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 | 0.064 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.099 | 0.075 |
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".