Career trajectories of nurses leaving the hospital sector in Ontario, Canada (1993–2004)
Bibliographic record
Abstract
AIM: This paper is a report of an analysis of the career trajectories of nurses 1 year after leaving hospitals. BACKGROUND: Although hospitals are traditionally the largest employers of nurses, technological advances and budgetary constraints have resulted in many countries in relative shrinkage of the hospital sector and a shift of care (and jobs) into home/community settings. It has been often assumed that nurses displaced from hospitals will move to work in the other workplaces, especially the community sector. METHOD: Employment patterns were tracked by examining a longitudinal database of all 201,463 nurses registered with the College of Nurses Ontario (Canada) between 1993 and 2004. Focusing on the employment categories Active (Working in nursing), Eligible-Seeking nursing employment or Dropout from the nursing labour market, year-to-year transition matrixes were generated by sector and sub-sector of employment, nurse type, age group and work status. FINDINGS: For every nurse practising nursing in any non-hospital job or in the community a year after leaving hospitals, an average of 1.3 and four nurses, respectively, dropped out of Ontario's labour market. The proportion of nurses leaving hospitals transitioning to the Dropout category ranged from 63.3% (1994-95) to 38.6% (2001-02). The proportion dropping out of Ontario's market was higher for Registered Practical Nurses (compared to Registered Nurses), increased with age and decreased with degree of casualization in nurses' jobs. CONCLUSION: Downsizing hospitals without attention to the potentially negative impact on the nursing workforce can lead to retention difficulties and adversely affects the overall supply of nurses.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".