The Effects of University Teaching Competency, Professor-Student Relationship, Professor-Colleague Relationship, and Self-Disclosure on Professors’ Job Stress
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of university teaching competency, professor-student relationship, professor-colleague relationship, and self-disclosure on professors’ job stress, as well as check the relationship between these variables and use it as basic data for preparing measures to reduce university professors' job stress. This study was conducted on university professors working at universities across the country who could understand and judge the contents of the survey and agreed to participate in this study. Data collection was from July 1 to July 31, 2021, after IRB approval, and a total of 129 data were used for the final analysis. The factors affecting the stress of teaching jobs are the director and dean (β=.259, p= .001), age (β=.258, p= .001), Professor-Co-Professor Relationship (β=.256, p=.001), self-opening (β= .178, p=.016), Faculty Competency Execution (β=.170, p=.It appeared in the order of 024), and among them, it was confirmed that the position (chief and dean) was the biggest influencing factor on the stress of the professors’ job. The explanatory power was 36.3%. Support for reducing job stress for university professors should be prepared, and more systematic and empirical discussions on the entire university professors need to be conducted.
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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 | 0.005 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".