Exploring the Emotional Intelligence Needs of University Lecturers in Managing Work-Related Challenges
Bibliographic record
Abstract
The emotional intelligence needs of Nigerian university lecturers in managing work‑related challenges were investigated in this study. A qualitative research approach of phenomenological interpretive design was used. Twelve university lecturers were selected purposively from three sampled Nigerian universities to participate in the research. The recorded interviews were transcribed and thereafter analysed with the assistance of ATLAS.ti 8 software. The four main themes that emerged were (a) struggling with regular work activities and emerging emotions, (b) finding a balance amidst many different demands and the lack of resources, (c) adaptability and adjustment problems, and (d) lack of emotional support from the university and possible reasons. The findings revealed that emotional intelligence needs regarding self-management and the management of relationships are present within the universities. The study also revealed that there is a need for institutional based policy to steer the addition of emotional intelligence exercise in academic systematic Professional improvement undertakings to ensure quality management of work challenges and the associated emotions. It is suggested that such training could be implemented through seminars and workshops in the various departments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".