Where are they going, and what can we do to keep them? Intent to leave among nurses in British Columbia, Canada
Bibliographic record
Abstract
Purpose. To identify: (1) alternate professions being considered by nurses, and (2) potential policy levers to retain them. Methods. This study describes responses to a subset of questions on a survey of nearly 15,000 nurses in British Columbia. Participants expressing intent to leave were asked what other professional options they were considering, and what changes they would need to keep them in nursing. We used thematic analysis to identify themes and sub-themes of participant responses. Results. Fewer than one in five nurses expressed intent to stay in the profession for more than two years. Participants cited a wide variety of other professional options available to them; the most commonly cited category was ‘anything but nursing’. When asked what they needed to stay in nursing, participants described improvements in compensation, safe staffing, work/life balance, workplace culture, physical and psychological safety, and opportunities for advancement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".