Investing in the Research Process: Nursing Health Services Research Unit - University of Toronto Site
Bibliographic record
Abstract
Linda O'Brien-Pallas, RN, PhD, FCAHS is a Professor in the Faculties of Nursing and Medicine at the University of Toronto and Director, Co-Founder and Co-Principal Investigator of the Nursing Health Services Research Unit (University of Toronto site). Dr. O'Brien-Pallas is acknowledged globally for her pioneering and innovative research in health human resources modelling, quality of work life for nurses and nursing workload measurement. The rigour of her research has been praised by respected researchers at international conferences, and her expertise is sought by governments and stakeholders at all levels in Canada and throughout the world. She is frequently called upon by the World Health Organization and the International Council of Nurses to provide high-level consultation on matters including midwifery and health human resources planning. Dr. O'Brien-Pallas has provided leadership to many boards and committees and is a co-founder of the Dorothy M. Wylie Nursing Leadership Institute, which received the 2003 Ted Freedman Award for Innovation in Education. She has received numerous awards for her research and innovative contributions to nursing, including the Canadian Nurses Association's prestigious Jeanne Mance Award in 2006.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.123 | 0.015 |
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".