An examination of advanced practice nurses’ job satisfaction internationally
Bibliographic record
Abstract
AIM: To examine the level of job satisfaction of nurse practitioners/advanced practice nurses in developing and developed countries. BACKGROUND: The nurse practitioner/advanced practice nurse has the advanced, complex skills and experience to play an important role in providing equitable health care across all nations. INTRODUCTION: Key factors that contribute to health disparities include lack of access to global health human resources, the right skill mix of healthcare providers and the satisfaction and retention of quality workers. METHODS: The study utilized a descriptive analysis and cross-sectional survey methodology with quantitative and qualitative sections of 1419 job satisfaction survey respondents from an online survey. RESULTS: Age, number of hours worked in a week and length of time that nurse practitioners/advanced practice nurses worked in their current jobs were statistically significant in job satisfaction. A key barrier was the lack of respect from supervisors and physicians. DISCUSSION: It was clear from the number of comments in the qualitative section of the survey that having a wide scope of practice is rewarding and challenging to the nurse practitioner and advanced practice nurse. CONCLUSION AND IMPLICATIONS FOR HEALTH POLICY: The challenges to transform healthcare gaps of access into a better distribution of health care in all countries would constitute a systematic change in policy including providing education and training for doctors and nurses that will match the skills needed in the workplace; emphasizing the right skill mix for the healthcare team; supporting advanced practice nurses in the workplace; and utilizing all healthcare providers to the fullest extent of their abilities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".