Motivational pathways of occupational and organizational turnover intention among newly registered nurses in Canada
Bibliographic record
Abstract
BACKGROUND: Staff turnover is a major issue for health care systems. In a time of labor shortage, it is critical to understand the motivational factors that underlie turnover intention in newly licensed nurses. PURPOSE: To examine whether different forms of motivation (the reasons for which nurses engage in their work) predict intention to quit the occupation and organization through distinct forms (affective and continuance) and targets (occupation and organization) of commitment. METHODS: Cross-sectional data were collected from a sample of 572 French-Canadian newly registered nurses working in public health care in the province of Quebec, Canada. The hypothesized model was tested by structural equation modeling. FINDINGS: Autonomous motivation (nurses accomplish their work primarily out of a sense of pleasure and satisfaction or because they personally endorse the importance or value of their work) negatively predicts intention to quit the profession and organization through target-specific affective commitment. However, although controlled motivation (nurses accomplish their work mainly because of internal or external pressure) is positively associated with continuance commitment to the occupation and organization, it directly predicts, positively so, intention to quit the occupation and organization. CONCLUSION: These results highlight the complexity of the motivational processes at play in the turnover intention of novice nurses, revealing distinct forms of commitment that explain how motivation quality is related simultaneously to intention to quit the occupation and organization. Health care organizations are advised to promote autonomous over controlled motivation to retain newly recruited nurses and sustain the future of the nursing workforce.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".