Exploring the Relationship Between Emotional Intelligence and Religiosity and the Experience of Emotional Labor in Working Women
Bibliographic record
Abstract
Women serve crucial roles within the home as caregivers and outside the home in the workforce, where they often fill many essential support positions such as service industry workers, teachers, social workers, nurses, and human service workers. In these roles, women must often mitigate the psychosocial issues of those whom they serve, resulting in high emotional labor with subsequent deleterious effects for them. Religiosity and emotional intelligence have been demonstrated to alleviate psychosocial stressors. Current research identified on emotional intelligence and emotional labor in diverse workspaces shows ongoing development. Biblical references to emotional intelligence and religiosity in emotional management highlight the need of addressing this issue for employed women. However, no research was identified that explored the connections between religiosity, emotional intelligence, and the experience of emotional labor among working women. This study sought to fill this research gap. Working women aged 18 years and older, living in Canada and the United States were surveyed via Amazon mTurk regarding their emotional intelligence, religiosity, and their emotional labor at home, and at work. The findings revealed a significant relationship between emotional intelligence and religiosity, as well as between religiosity and emotional labor. Furthermore, it identified a marked difference in emotional labor experienced between home and work environments. These findings offer benefits for future research, for psychological practice and consulting, as well as for organizations in improving the work-life balance of employed women.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".