Bibliographic record
Abstract
an issue of importance to many national and provincial/territorial nursing organizations and, at the same time, not a central priority for anyone. Opportunities have undoubtedly been lost because of the lack of concerted or coordinated effort to advance the importance of nursing research in addressing quality of care, patient safety, health promotion and a myriad of other topics that affect the health of Canadians and the provision of healthcare. Over the past few years there has been increasing recognition that advocacy for nursing research is a responsibility that requires a collaborative approach. With the guidance and support of the Office of Nursing Policy (Health Canada) and the substantive assistance of Leslie Degner, RN, PhD, Denise Alcock, RN, PhD and Pat Griffin, RN, PhD, the Canadian Nurses Association (CNA), the Canadian Nurses Foundation (CNF), the Canadian Association of Schools of Nursing (CASN), the Canadian Association for Nursing Research (CANR) and the Academy of Canadian Executive Nurses (ACEN) determined to work together and share a leadership role in advancing nursing research and innovation in Canada. To this end they have formed the Canadian Consortium for Nursing Research and Innovation. The significance of this initiative may best be seen by putting it within a historical context. Unlike our colleagues to the south, who have an Institute for Nursing Research within the National Institutes of Health, Canadian nurse researchers have never had a dedicated source of funding for research. We also were slower than the United States and some other countries in establishing doctoral programs in nursing, and hence did not have a critical mass of potential researchers. According to the Canadian Institute for Health Information (2002), there were 671 doctorally prepared nurses in Canada in 2001 (this repreShared Leadership for Nursing Research
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 | 0.182 | 0.330 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.049 | 0.020 |
| Open science | 0.008 | 0.059 |
| Research integrity | 0.014 | 0.063 |
| Insufficient payload (model declined to judge) | 0.039 | 0.028 |
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".