Pay Satisfaction, Job Satisfaction and Turnover Intent
Bibliographic record
Abstract
The purpose of this paper was to examine the relationships among pay satisfaction, job satisfaction, and turnover. While there is a fairly large body of literature on pay satisfaction/dissatisfaction-turnover relationship, there are reasons to expect different outcomes in occupations – such as social work and nursing – where job satisfaction, versus pay, may be of equal, if not greater importance. Essentially, it may be argued that in these sectors, workers are driven more by job satisfaction rather than their paychecks. Yet, there is little empirical research on this issue; thus, a primary purpose of this study is to address this research need. This study will add to the recent research that has focused on key human resources management and industrial relations issues related to the nursing profession in Canada. Furthermore, many studies use a unidimensional measure of pay satisfaction even though the literature suggests that there are better measures. Using a four-dimensional instrument in this study, we improve on past practices. Using a sample of 200 nurses in a unionized hospital in Ontario to test our hypotheses, we found support for both (viz., 1. The four pay dimensions will affect turnover intent differently; and 2. Job satisfaction will add incrementally to the explained variance in the pay satisfaction-turnover relationship). The findings support the contention that nurses may be more motivated by their jobs, versus their pay. The findings may be good news for organizations that want to better manage labour costs. There are different ways for hospitals to improve their workplace environment in order to increase satisfaction with intrinsic job factors and reduce turnover.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".