New Graduate Nurses’ Professional Commitment: Antecedents and Outcomes
Bibliographic record
Abstract
PURPOSE: This study examines the factors that increase new graduate nurses' professional commitment and how this professional commitment in turn affects professional turnover intentions, anxiety, and physical health symptoms. DESIGN: The study was carried out in association with the nursing undergraduate's affiliation of Quebec, Canada. A three-wave longitudinal design was employed among nursing students. Nurses were surveyed before they entered the labor market, and then twice after they started working. METHODS: Participants were contacted by post at their home address. The hypotheses were tested using structural equation modeling. FINDINGS AND CONCLUSION: Professional commitment explains why good work characteristics and the provision of organizational resources related to patient care reduce nurses' anxiety and physical symptoms, and increase their professional turnover intentions. Pre-entry professional perceptions moderate the effects of work characteristics on professional commitment such that when participants hold positive pre-entry perceptions about the profession, the propensity to develop professional commitment is higher. CLINICAL RELEVANCE: There is a worldwide shortage of nurses. From a nurse training perspective, it is important to create realistic perceptions of the nursing role. In hospitals, providing a good work environment and resources conducive to their professional ethos is critical for ensuring nurses do not leave the profession early on in their careers.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".