Rising Up to Embrace Multi-Faceted and Dynamic Retention Challenges
Bibliographic record
Abstract
This issue is the last of the three-part series focused on the critically important challenge of nurse retention. The articles that we have selected span a range of topics from the personal to political with implications for readers in leadership positions across nursing practice, policy and education. What we are learning is that retention is as multi-faceted and dynamic as the times we are living in. There is no one-size-fits-all solution, no Holy Grail - if we could only find it - that will turn the tide of exodus from the profession. Retention is fundamentally about valuing nurses and demonstrating that worth in concrete tangible ways that are meaningful to nurses as a group and as individuals. It is a tall order that can only be achieved with leadership that embraces the unprecedented challenges we are living through as windows of opportunity to lean into and make transformative changes that will engage nurses and benefit local and global health.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".