Assessing longitudinal change of and dynamic relationships among role stressors, job attitudes, turnover intention, and well‐being in neophyte newcomers
Bibliographic record
Abstract
Abstract Using a latent growth modeling (LGM) approach, this paper examines the trajectories of change in role stressors (ambiguity, conflict, and overload), job attitudes (affective commitment and job satisfaction), and turnover intention and psychological well‐being among neophyte newcomers, as well as the relationships among these changes. Based on a sample of 170 university alumni surveyed three times during the first months of employment, we found that role conflict and role overload increased, affective commitment and job satisfaction declined, and turnover intention increased over the course of the study. Role ambiguity and well‐being did not change. The initial levels of affective commitment, job satisfaction, and well‐being were positively related to the increase in role overload, while the initial level of turnover intention was related to a reduced increase in role overload over time. We also found that the increase in role overload and role conflict was associated with a decline in affective commitment and job satisfaction, respectively, and that the decrease in affective commitment and satisfaction was related to an increase in turnover intention. We discuss the implications of these findings. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".