Determinants of hospital nurse intention to remain employed: broadening our understanding
Bibliographic record
Abstract
AIM: This paper is a report of a study to identify nurse reported determinants of intention to remain employed and to develop a model explaining determinants of hospital nurse intention to remain employed. BACKGROUND: A worsening shortage of nurses globally suggests that efforts must be made to promote retention of nurses. However, effective retention promotion strategies depend on understanding the factors influencing nurse retention. METHODS: A descriptive study using focus group methodology was implemented. Thirteen focus groups including 78 nurses were carried out in two Canadian provinces in 2007. Thematic analysis strategies were incorporated to analyse the data. FINDINGS: Eight thematic categories reflecting factors nurses described as influencing their intentions to remain employed emerged from focus groups: (1) relationships with co-workers, (2) condition of the work environment, (3) relationship with and support from one's manager, (4) work rewards, (5) organizational support and practices, (6) physical and psychological responses to work, (7) patient relationships and other job content, and (8) external factors. A model of determinants of hospital nurse intention to remain employed is hypothesized. CONCLUSION: Findings were both similar to and different from previous research. The overriding concept of job satisfaction was not found. Rather, nurse assessments of satisfaction within eight thematic categories were found to influence intentions to remain employed. Further testing of the hypothesized model is required to determine its global utility. Understanding determinants of intention to remain employed can lead to development of strategies that strengthen nurse retention. Incorporation of this knowledge in nurse education programmes is essential.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".