Gold standard research and evidence applied: The Inspire Nursing Leadership Program
Bibliographic record
Abstract
Billions of dollars are invested annually in leadership development globally; however, few programs are evidence-based, risking adverse outcomes, and wasted time and money. This article describes the novel Inspire Nursing Leadership Program (INLP) and the outcomes-based process of incorporating gold standard evidence into its design, delivery, and evaluation. The INLP design was informed by a needs analysis, research evidence, and by nursing, Indigenous, and equity, diversity, and inclusion experts. The program's goals include enabling participants to develop leadership capabilities, cultivate strategic community partnerships, lead innovation projects, and connect with colleagues. Design features include an outcomes-based approach, the LEADS framework, and alignment with the principles of adult learning. Components include leadership impact projects, 360-assessments, blended interactive sessions, coaching, mentoring, and application and reflection exercises. The evaluation framework and subsequent proposed research design align to top-quality standards. Healthcare leadership programs must be evidence-based to support leaders in improving and transforming health systems.
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 | 0.449 | 0.606 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".