The Nursing Human Resource Planning Best Practice Toolkit: Creating a Best Practice Resource for Nursing Managers
Bibliographic record
Abstract
Evidence of acute nursing shortages in urban hospitals has been surfacing since 2000. Further, new graduate nurses account for more than 50% of total nurse turnover in some hospitals and between 35% and 60% of new graduates change workplace during the first year. Critical to organizational success, first line nurse managers must have the knowledge and skills to ensure the accurate projection of nursing resource requirements and to develop proactive recruitment and retention programs that are effective, promote positive nursing socialization, and provide early exposure to the clinical setting. The Nursing Human Resource Planning Best Practice Toolkit project supported the creation of a network of teaching and community hospitals to develop a best practice toolkit in nursing human resource planning targeted at first line nursing managers. The toolkit includes the development of a framework including the conceptual building blocks of planning tools, manager interventions, retention and recruitment and professional practice models. The development of the toolkit involved conducting a review of the literature for best practices in nursing human resource planning, using a mixed method approach to data collection including a survey and extensive interviews of managers and completing a comprehensive scan of human resource practices in the participating organizations. This paper will provide an overview of the process used to develop the toolkit, a description of the toolkit contents and a reflection on the outcomes of the project.
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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 | 0.047 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".