Descriptive predictors of nursing faculty’s job satisfaction
Bibliographic record
Abstract
Introduction: Nursing shortages are directly impacted by the growing number of faculty vacancies in the United States. Many factors contribute to these vacancies including age, retirement, compensation, lack of funding for positions, marketplace competition, geographical area, lack of qualified applicants, and workload. The retention of qualified nursing faculty is crucial to the future health care system and to higher education institutions with nursing programs. Identifying work factors that consistently influence faculty members' intentions to remain in academia is crucial to ensuring public health with a robust nursing workforce of the future. The purpose of this article is to present an overview of the literature related to determining job satisfaction and job descriptive work-engagement levels of individuals who are employed as higher education faculty members in the field of nursing.Description: Retention efforts for nursing faculty, due to shortage, have become necessary to examine how faculty perceive their engagement with teaching. A descriptive, correlational project study design was performed using an invitation to complete an online survey via Qualtrics as a part of a larger study of nursing faculty. This article will examine six predictor variables of the Job Descriptive Index (JDI) and the Job in General (JIG).Discussion: This study explored the job descriptive index and job satisfaction of faculty in nursing schools as it relates to an assortment of descriptive variables, including demographics, pay, supervisors, rank, peers, and workload reflections. Nursing faculty perceptions of promotion opportunities, salaries, resources, and support play an important role in attracting, hiring, and retaining nursing faculty, as shown by the results of this study. The findings from this study can serve higher education institutions in ascertaining the satisfaction variables that can be altered to attract and retain faculty in nursing.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".