Research on the Relationship between Trainers' Turnover Intention and Organizational Justice
Bibliographic record
Abstract
The aim of this study was to investigate the relationship between the organizational justice levels and the turnover intention of trainers working in different departments. The present research was designed with relational screening model. Organizational Justice Scale developed by Kim (2009) and adapted to Turkish by Sayın and Şahin (2017) and the Turnover Intention Scale developed by Mobley et al. (1978) were applied to 382 volunteer participants. One-way analysis of variance was used in order to determine whether there was a significant difference in turnover intention and organizational justice levels according to demographic characteristics of participants. Pearson correlation coefficient was calculated in order to determine the level of relationship between the participants' organizational justice levels and their turnover intention. Significance level was taken as 0.05. At the end of the study, it was determined that there was a negative and medium level relationship between the organizational justice levels of the trainers and their turnover intention. When evaluating them in terms of demographic variables, it was determined that as the level of educational level of the trainers increases, the level of distribution justice sub-dimension decreases and the levels of organizational perception of the trainers, whose branches are the combat sport, were low.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".