Factors influencing intention to continue employment in Japanese hospital nurses working at tertiary emergency medical facilities
Bibliographic record
Abstract
Objective: The study purpose was to investigate influencing factors related to nurses’ intention to continue employment in tertiary emergency medical facilities.Methods: A self-report questionnaire survey was conducted, and responses were collected by mail. We investigated seven factors associated with the intention to remain employed that were determined by preliminary research. Data were analyzed using a covariance structure analysis.Results: Of the 561 responses received, 461 were found to be valid for analysis. A model showing relationships among the five factors (organizational commitment, job stress, job satisfaction, nurse-physician collaboration, and intention to remain employed) was created. Organizational commitment and job stress were directly related to intention to continue employment, while, nurse-physician collaboration demonstrated effects on the entire model.Conclusions: The strongest factor observed was organizational commitment. The types of institutions examined in the present study almost exclusively treat seriously ill patients. This may explain why nurse-physician collaboration affected the entire model. In a tertiary emergency facility, a nurse can more easily play a critical role in the healthcare process. In the future, it will be important to consider these factors when creating an organizational climate conducive to continued employment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".