An online nursing leadership literature centre at the University of Manitoba Health Sciences Libraries
Bibliographic record
Abstract
BACKGROUND: Decades of hospital restructuring in Canada resulted in significant reductions of nursing leadership positions and altered a nursing infrastructure important for guiding patient care. The importance of acquiring nursing leadership skills to address the negative effects of restructuring is advocated by Canadian nursing bodies. OBJECTIVES: To describe a service innovation for a nursing community. The librarians of the University of Manitoba Health Sciences Libraries (UMHSL) created an online nursing leadership literature centre to support a leadership programme launched by the Nursing Leadership Council (NLC) of the Winnipeg Regional Health Authority. The article will contribute to the body of literature about health library services for nurses. METHODS: The creation of the service is described. A literature search was undertaken to determine what services have been implemented by librarians for nursing leadership programmes, as well as to review the literature with regard to contributions made by librarians for nursing communities. RESULTS: The literature service, comprised of 19 webliographies based on the NLC's leadership topics, is available on the UMHSL website. A webliography, by definition, is a list of electronic works relating to a particular topic. The NLC created its own website that provides nurses with a means to identify, enhance and evaluate leadership competencies, and which is linked to the UMHSL website. CONCLUSION: The contributions of the UMHSL librarians to this project support the goals of instilling leadership skills in nurses, encouraging evidence-based nursing practice, and transforming a practice environment to meet the ultimate goal of effective patient care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.140 | 0.040 |
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".