Nurse intention to remain employed: understanding and strengthening determinants
Bibliographic record
Abstract
AIM: This paper reports a study testing a hypothesized model of the determinants of nurse intention to remain employed in current hospitals of employment. BACKGROUND: Previous research has shown that stronger nurse intention to remain employed is associated with higher job satisfaction, higher organizational commitment, higher perceived manager support, lower burnout, higher work group cohesion, being older, having more years of nursing experience and having lower levels of education. METHODS: A descriptive survey design was adopted. Over 13,000 Ontario, Canada nurses were invited to complete a mailed survey between February and May 2003. The Ontario Nurse Survey includes instruments and items measuring job satisfaction, burnout, professional nursing practice environment, demographic characteristics of nurse respondents and items about intention to remain employed. Two multiple regression models, one including all variables and the other using a stepwise method, were used to test the proposed model. RESULTS: Regression models explained 34% of variance in nurse intention to remain employed. The strongest predictors were nurse age, overall nurse job satisfaction and years of employment in the current hospital. Although the proposed model hypothesized six categories of predictors of intention to remain employed, only four of these were statistically significant determinants of nurse intention to remain: job satisfaction, personal characteristics of nurses, work group cohesion and collaboration, and organizational commitment of nurses. The other two categories of predictors, nurse burnout and nurse manager ability and support, may be predictors of job satisfaction and have indirect effects on intention to remain employed that are mediated through job satisfaction. CONCLUSION: Possible strategies to strengthen predictors of intention to remain employed include employment practices that reflect moral integrity, incorporate clear communication systems, maximize employee involvement in decision-making, promote praise and recognition, and establish a shared vision and goals.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".