The KRESCENT Program: An initiative to match supply and demand for kidney research in Canada
Bibliographic record
Abstract
BACKGROUND: The prevalence of kidney disease is rising in Canada, and new approaches to prevention, diagnosis, and treatment are required. A kidney research training strategy, which enhances capacity while fostering collaboration and knowledge translation, may help to address this health care problem. PURPOSE: This manuscript describes the Kidney Research Scientist Core Education and National Training (KRESCENT) Program that was launched in 2004 with a major goal to enhance kidney research capacity in Canada. FEATURES: KRESCENT is an innovative training program, which recruits from a variety of research disciplines, and emphasizes multi-disciplinary research approaches, team-based collaboration and knowledge translation. The program provides salary support for post-doctoral fellows, new investigators and allied health doctoral trainees, and also offers core curriculum and mentorship support. The curriculum involves knowledge acquisition, application and integration and uses workshops and web-based problem modules to enhance research skills. Training in methodological approaches and career development is also included. Initial evaluation of KRESCENT suggests that kidney research capacity in Canada has increased, and trainees have a high success rate in obtaining academic positions (~88%) and peer-review grant support (~50%). SUMMARY: KRESCENT represents a novel collaborative approach to kidney research training in Canada that may serve as a suitable model for training in other countries, or in other medical disciplines.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
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".