Examining the Influence of Key Demographic Variables of Preservice Teachers in a University in Ghana on Their Emotional Intelligence
Bibliographic record
Abstract
This study is relevant as it highlights the crucial role of emotional intelligence (EI) in achieving long-term success and managing stress at work. The impact of demographic diversity on EI, a topic of ongoing debate, is the focus of our research. We employed a cross-sectional survey approach to investigate the potential influence of demographic variables such as age, gender, residential status, religious affiliation, and program of study on the EI of preservice teachers at the University of Ghana. We collected data from 291 participants using a self-made questionnaire and used descriptive and inferential statistics for analysis. The results showed that the only factor that significantly affected EI was age. Given that age was the only significant factor affecting EI in our study, the implications for raising EI among preservice teachers may need to be reconsidered. While our findings do not support broad demographic influences, they highlight the importance of age-related experiences in developing EI. This finding underscores the urgent need for age-inclusive policies and targeted EI development strategies in teacher training programs. These policies should be in line with the postpositivist worldview. Data was collected carefully to safeguard the participants' identities, and ethical approval was obtained. The findings highlight the value of motivating preservice teachers to live stress-free lives, engage in social activities, and maintain a healthy lifestyle to improve their emotional intelligence, self-control, social awareness, and interpersonal skills and, ultimately, increase their efficacy as future teachers.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".