The employment, retention and exit of publicly employed nurses in New Brunswick, Canada: An analysis using linked administrative data
Bibliographic record
Abstract
ObjectivesAs in many jurisdictions, New Brunswick (Canada) is facing an acute and continuing shortage of registered nurses as recruitment fails to keep pace with retirements and resignations. The purpose of this retrospective study is to analyze the recruitment, retention and exit decisions of nurses in the NB public health system.
 MethodsThe analysis will use a unique linked administrative data set that combines individual-level nurse employment data, immigration landing records, university graduation data and Medicare health insurance registry data on all publicly employed registered nurses in NB as well as individuals who graduated from a University nursing program in NB and immigrants to NB who previously trained as nurses in their home country. Data are provided by multiple government departments and are accessed through the NB Institute for Research, Data and Training. The analysis will include both descriptive statistics and econometric methods appropriate to the particular outcome of interest.
 ResultsThe analysis will present results on four dimensions of nursing employment. The first is transitions from nursing programs in NB universities into employment in the NB public health system. The second is transitions of internationally educated nurses into employment as nurses in the NB public system and the timing of those transitions, which will reflect the process of credential evaluation, training and licensure. The third is exits from employment in the public health system, with consideration of both retirement and pre-retirement departures. The fourth is mobility decisions of those nurses exiting employment and whether they remain in the province after leaving employment. The potential effects of a range of demographic, geographic and health system level factors on these outcomes will be considered.
 ConclusionAnalysis of entry to and exit from nursing employment in NB and factors associated with those dynamics will be vital for health resource planning for a province dealing with growing labour shortages. The unique nature of the linked data will also generate important insights for other jurisdictions facing similar challenges.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".