Emotional Intelligence and Quality of Working Life at Federal Institutions of Higher Education in Brazil
Bibliographic record
Abstract
People have prioritized even more the quality of life in the most diverse places of work. Quality of Working Life (QWL) can be analyzed from some indicators and factors that help to evaluate the workplace and the people who work on it. In addition, the personal characteristics also interfere in the QWL perception level. In this context, in this article, the objective is to analyze the emotional intelligence (EI) and Quality of Working Life factors in the professors’ work at federal institutions of higher education in Brazil. The data were collected by a questionnaire composed of scales to identify some variables of individual differences and QWL factors. The survey instrument was sent via Survey Monkey to university professors from 16 federal higher education institutions in the Southeast, Midwest, and Federal District. Once downloaded, the data were analyzed using SPSS software version 21. After the analysis, it is realized that EI can be analyzed from five components: well-being, self-control, emotionality, sociability and emotions recognition. It was observed that there are significant correlations between Emotional Intelligence and QWL factors. Furthermore, there are significant relations between the life events, EI and QWL factors.
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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 | 0.004 |
| 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 | 0.000 |
| 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".