Correlates and Consequences of Nursing Staff Job Insecurity
Bibliographic record
Abstract
The health care system, and hospitals, underwent considerable restructuring and downsizing in the early to mid-1900s in several countries as governments cut costs to reduce their budget deficits. Studies of the effects of these efforts on nursing staff and hospital functioning in various countries generally reported negative impacts with threats to job security emerging as an important outcome of these changes. Health care restructuring and hospital downsizing is again being implemented as governments struggle to reduce deficits at a time of worldwide economic recession in 2008/2010. This study examines correlates and consequences of job insecurity among Canadian nursing staff, with a focus on nurses’ well-being. Data were collected from 290 nursing staff working in hospitals in Ontario, Canada. Feelings of job insecurity in the sample as a whole were relatively low. Personal demographics and work situation characteristics were generally uncorrelated with feelings of job insecurity. Consistent with previous findings, perceived job insecurity was once again associated with less favorable work and well-being outcomes. Some suggestions for more successful approaches to addressing levels of subjective job insecurity are offered.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".