Job Insecurity in Nursing: A Bibliometric Analysis
Bibliographic record
Abstract
Nurses are a key workforce in the international health system, and as such maintaining optimal working conditions is critical for preserving their well-being and good performance. One of the psychosocial risks that can have a major impact on them is job insecurity. This study aimed to carry out a bibliometric analysis, mapping job insecurity in 128 articles in nursing, and to determine the most important findings in the literature. The search was conducted in the Web of Science Core Collection database using the Science Citation Index (SCI)-Expanded and Social Sciences Citation Index (SSCI) indexes on 6 March 2020. This field of discipline has recently been established and has experienced significant growth since 2013. The most productive and widely cited authors are Denton and Zeytinoglu. The most productive universities are Toronto University, McMaster University, and Monash University. The most productive countries are the United States, Canada, Australia, Finland, and the United Kingdom. The most widely used measure was Karasek's Job Content Questionnaire (JCQ). The main findings report negative correlations with job satisfaction, mental well-being, and physical health. Job insecurity is a recent and little-discussed topic, and this paper provides an overview of the field. This will enable policies to reduce psychosocial risks among nurses to be implemented.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.185 | 0.219 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".