A comparative review of nurse turnover rates and costs across countries
Bibliographic record
Abstract
AIMS: To compare nurse turnover rates and costs from four studies in four countries (US, Canada, Australia, New Zealand) that have used the same costing methodology; the original Nursing Turnover Cost Calculation Methodology. BACKGROUND: Measuring and comparing the costs and rates of turnover is difficult because of differences in definitions and methodologies. DESIGN: Comparative review. DATA SOURCES: Searches were carried out within CINAHL, Business Source Complete and Medline for studies that used the original Nursing Turnover Cost Calculation Methodology and reported on both costs and rates of nurse turnover, published from 2014 and prior. METHODS: A comparative review of turnover data was conducted using four studies that employed the original Nursing Turnover Cost Calculation Methodology. Costing data items were converted to percentages, while total turnover costs were converted to US 2014 dollars and adjusted according to inflation rates, to permit cross-country comparisons. RESULTS: Despite using the same methodology, Australia reported significantly higher turnover costs ($48,790) due to higher termination (~50% of indirect costs) and temporary replacement costs (~90% of direct costs). Costs were almost 50% lower in the US ($20,561), Canada ($26,652) and New Zealand ($23,711). Turnover rates also varied significantly across countries with the highest rate reported in New Zealand (44·3%) followed by the US (26·8%), Canada (19·9%) and Australia (15·1%). CONCLUSION: A significant proportion of turnover costs are attributed to temporary replacement, highlighting the importance of nurse retention. The authors suggest a minimum dataset is also required to eliminate potential variability across countries, states, hospitals and departments.
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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 | 0.018 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.031 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".