Facility Turnover and Vacancy Rates of Registered Nurses: Do They Predict How Nurses Are Recruited?
Bibliographic record
Abstract
Objectives: Healthcare organisations in Western industrialised countries are experiencing nursing labour markets characterised by extreme staff shortages and high levels of turnover and vacancy for Registered Nurses (RNs). The effective recruitment and retention of nursing personnel are considered an essential management function if healthcare organisations wish to survive and prosper in these difficult times. The objective of this study is to examine the relationship between healthcare establishment turnover and vacancy rates of RNs and the means these establishments use to recruit nursing personnel. It is predicted that in the face of higher turnover and vacancy rates for registered nurses, healthcare organisations will utilise more active (employer-initiated) and fewer passive (employee-initiated) recruitment channels. Method: Data for this study were collected from over 700 hospital and nursing homes in Canada. Directors of Nursing at these establishments were asked about the use of various recruitment channels to attract nursing personnel. Results: Bi- and multi-variate analyses were performed to characterise the relationships between establishment RN turnover and vacancy rates with respect to the selection of recruitment channel utilised. Ordinary Least Square regression analysis showed that perceived vacancy rate, and to a lesser degree turnover of RNs, were strong predictors of the use of more active recruitment channels. Healthcare organisations with a local labour market characterised by a greater supply of employable RNs, were more likely to use more passive channels, even in the face of higher RN turnover and vacancy. Healthcare organisations which were perceived as being stronger 'employers-of-choice,' were also more likely to use more passive recruitment channels, even in the face of higher vacancies for RNs. Conclusion: Results from this study suggest that when labour markets have a larger surplus of RNs for potential employment, establishments are less than proactive in their attempts to vigorously recruit. During these times, having a perception of being a strong employerof- choice, enables healthcare organisations to maintain full employment without having to launch aggressive recruitment initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".