Happenings / L_Événement: Nurses and the Canadian Institutes of Health Research
Bibliographic record
Abstract
When the Canadian Institutes of Health Research was initiated in June 2000, nurse researchers wanted and expected greater access to research funding than had been awarded through the Medical Research Council but were not convinced that increased funding for health research in Canada would translate into greater access to funding to support nursing research. The CIHR is now in its second year. Within the first year of its existence, a Governing Council, 13 Institutes, and 13 Institute Advisory Boards were established. Governing Council members, Scientific Directors, and members of Institute Advisory Boards are chosen not on the basis of their specific disciplines but rather on the basis of their expertise and their track record. Therefore, nurses should be proud that at least 17 registered nurses contribute to the decisional and advisory infrastructure of the CIHR. Nurses also serve as CIHR university delegates and on peer-review committees. It is important that nurses maintain a high profile on the CIHR. Nominations for participation on review committees are invited through the research vice-president (or equivalent) of educational or health-research institutions, or through the CIHR university delegate.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.017 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 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".