Bibliographic record
Abstract
The integration and utilization of Chief Nurses (CNs) to lead and support complex health systems and health workforce optimization in governments, nongovernmental organizations, and across all health and social sectors is of paramount importance. At a time when the global nursing profession is challenged with a strained workforce and a growing leadership gap, it is essential that we examine and pay attention to the importance of the CN role. To attain universal health coverage, sustainable development goals, and global health, the need for the right number and a well-prepared nursing workforce is evident. While nursing recruitment and retention are key for system sustainability, it requires the right governance, leadership, infrastructure, and resources. A System CN can lead, advise, and impact the success of the nursing workforce in collaboration with senior leadership teams. At this time of major health challenges, too many health and social systems, both at government and nongovernmental levels, do not have a CN at all, or the role is delegated to a staff position with limited ability to impact the system on the local, regional, national, and global scale. Recruitment and investments targeting CNs are required to resuscitate, stabilize, and invigorate knowledgeable leaders who can transform, inspire, and maximize nursing contributions to assure access and quality healthcare. My personal and professional CN journey is highlighted here as an illustration for nurses who lead formally and informally, as they contemplate their own current and future ambitions and contributions.
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 | 0.034 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".