Predictors of registered nurses' organizational commitment and intent to stay
Bibliographic record
Abstract
BACKGROUND: Health care reform has significantly altered employment relations. Research findings suggest that the presence or absence of supportive work environments helps explain the differences observed in employee attitudes and turnover intentions. PURPOSES: The purposes of this study were to examine frontline registered nurses' (RNs') perceptions of organizational culture and attitudes and behaviors and test a model linking culture to outcome (organizational commitment and intent to stay). METHODOLOGY: A non-experimental predictive survey design was used to test the model in a sample (N = 343) of acute care RNs employed in one Canadian province. Data were collected with the following scales: Emotional Climate, Practice Issues, Collaborative Relations, Psychological Contract Violation, General Job Satisfaction, Organizational Commitment Questionnaire, and Intent to Stay. FINDINGS: The response rate was 29.4%. Most respondents were middle aged and diploma prepared, were in their current positions for 5 years or more, had 10 or more years of nursing experience, and worked full time. Despite moderate levels of job satisfaction, RNs held negative perceptions of culture (emotional climate, practice-related issues, and collaborative relations), trust, and commitment and were unlikely to stay with current employers. Structural equation modeling provided support for the impact of culture, trust, and satisfaction on commitment and partial support for intent to stay, explaining 45 and 31% of the variance, respectively. PRACTICE IMPLICATIONS: The development and implementation of policies and interventions aimed at creating more supportive work environments and greater trust in employers and job satisfaction have merit. The most obvious benefit from such strategic interventions is the potential for improving RNs' organizational commitment and reducing turnover intentions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".