Work Turnover and Its Impact on the Quality of Productivity in the Industrial Sector
Bibliographic record
Abstract
The aim of this study was to identify the effect of high turnover on quality of productivity in the industrial sector, and to propose appropriate solutions to reduce the reasons for leaving work. The motivation to carry out this study, the spread of the turnover phenomenon leading to dysfunction is in the interest of the organization, and has a direct impact on the decline in the quality of productivity of the organization, so was applied to the industrial sector to know and measure this impact. The methodology used in this study is the descriptive and analytical approach and the case study methodology.The results of the study show that there is a relationship of statistical significance between the average level of performance of the employee and the level of total productivity in the organization. And that there is a middle relationship between job stability and employee performance level, and that there is a negative relationship between the turnover rate and quality of productivity in the industrial sector. The study recommends that employers try to retain existing workers and provide appropriate incentives for them to reduce the rate of turnover in institutions, because turnover is a cost to institutions that can be eliminated by retaining staff and institutions should improve financial and non-financial compensation in proportion to the circumstances And staff needs to ensure job stability and ensure quality of productivity.
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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 | 0.003 | 0.014 |
| 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 | 0.002 | 0.001 |
| 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".