Predicting nurses' occupational commitment and turnover intention: The role of autonomous motivation and supervisor and coworker behaviours
Bibliographic record
Abstract
AIM: To examine whether supportive supervisor (transformational leadership) and coworker (autonomy-supportive) behaviours predict occupational commitment and turnover intention over time through autonomous motivation. BACKGROUND: Nurse turnover is a serious issue in several countries, straining the efficiency of the healthcare system and compromising both the quality and accessibility of healthcare. METHOD: Longitudinal data were collected over 12 months from 387 French-Canadian registered nurses. Structural equation modeling was used to test the hypothesized model. RESULTS: The relationships between predictors at Time 1 (supervisor and coworker behaviours) and occupational commitment and turnover intention at Time 2 are mediated by autonomous motivation at Time 1. CONCLUSION: In times of global scarcity, the present findings provide insights into how the healthcare work environment acts on nurses' occupational turnover and commitment. IMPLICATIONS FOR NURSING MANAGEMENT: Healthcare organizations are advised to foster supportive work environments and promote autonomous motivation to sustain the nursing workforce.
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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".