Happenings - Burgeoning Opportunities in Nursing Research
Bibliographic record
Abstract
Research opportunities for nurses in Canada have never been greater. Federal government funding of health research, including nursing research, has increased substantially in recent years. The priorities of many funding agencies have shifted to include a strong emphasis on interdisciplinary collaborative teams, providing an even greater opportunity for nurses to share in the research experience. In spite of the opportunities, the number of nurses applying for Canadian Institutes of Health Research (CIHR) and Canadian Health Services Research Foundation (CHSRF) research funds as principal investigators remains low relative to other disciplines. The application success rate for nurses is also lower than the overall average. New initiatives by CIHR and CHSRF are explicitly focused on building the capacity of Canadian nurses to lead and contribute to emerging and established research agendas. These initiatives, which are the subject of this paper, may be a remedy for the low level of research funding in nursing.
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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 | 0.103 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.025 | 0.038 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".