Bibliographic record
Abstract
As a current member of ACEN, I am pleased to have an opportunity in this column to profile briefly an upcoming CHSRF-funded study of Canadian nursing leadership. The study is officially entitled “A Profile of the Structure and Impact of Nursing Management in Canadian Hospitals,” and was approved for funding by the Canadian Health Services Research Foundation (Project Identifier: RC10964-06) in September 2003. I am co-principal investigator with Dr. Heather Laschinger, Professor, University of Western Ontario. The overall goal of this three-year study is to profile current nursing leadership and management structures in Canadian hospitals by examining the organizational and structural characteristics of nursing management roles (at senior, middle and unit levels) in teaching and community hospitals. This study will also examine how nursing leadership roles in hospital settings across Canada have changed and the factors that are important in enabling nurse leaders to perform their roles. A number of coinvestigators from various disciplines, in addition to nursing and decision-maker partners and consultants (see below), form a strong team to conduct this 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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.030 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".