UNIVERSITY OF MANITOBA MANITOBA CENTRE FOR NURSING AND HEALTH RESEARCH BACKGROUND
Bibliographic record
Abstract
was developed in response to a review of the Manitoba Nursing Research Institute (MNRI) by the Senate Committee on University Research This proposal was developed as a plan to re-organize and revitalize the MNRI, and to comply with the University of Manitoba Policy on Research Centres, Institutes and Groups The name of the Centre/Institute has been changed to reflect a broader interdisciplinary focus on health research, and to have wider appeal for health care professionals from disciplines other than nursing to become members of the Centre. “Manitoba ” is retained in the name because the Centre is envisioned to play a key role in promoting nursing and health research throughout the province. The MCNHR will remain based within the Faculty of Nursing. Mission, Vision and Goals of the Manitoba Centre for Nursing and Health Research Mission: To create an environment that promotes and supports the conduct, dissemination and uptake of collaborative nursing and health research to benefit the health of Manitobans and beyond. Vision: Members of the Manitoba Centre for Nursing and Health Research will be recognized for excellence and leadership in nursing and health research provincially, nationally and internationally. Goal 1: To foster the conduct of high quality nursing and health research by members of the centre. Strategies: • Invite members from faculties, agencies and organizations who will serve to promote the mission and goals of MCNHR, and have knowledge and/or interest in nursing and health research issues. • Provide a comprehensive system of research support services to research and professional affiliates. • Foster the development and implementation of research projects related to selected areas of research concentration:
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.137 | 0.035 |
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".