High Level of Emotional Intelligence is Related to High Level of Online Teaching Self-Efficacy among Academic Nurse Educators
Bibliographic record
Abstract
This study examined the relationship between emotional intelligence (EI) and online teaching self-efficacy among 115 academic nurse educators who teach online (totally, blended, or both). The sample was randomly drawn from the list of nursing schools accredited by Commission on Collegiate Nursing Education (CCNE) with baccalaureate, master’s and/or doctoral programs. The hypothesis tested states “Academic nurse educators who teach online and who report higher levels of EI would also report greater online teaching self-efficacy.” Results showed a significant moderate relationship (r=0.446, p< .01) between EI and online teaching self-efficacy. The coefficient of determination R2 was 0.199, which indicates that about 20% of the variation in online teaching self-efficacy can be explained by EI contribution. The hypothesis was supported. Results also indicated that online teaching self-efficacy was positively related to duration of being an academic nurse educator (r = 0.212, p<0.05) and duration of teaching online (r = 0.203, p< 0.05). Further, there was no significant difference between the different age groups regarding EI and online teaching self-efficacy. Similarly, there was no significant difference among university degrees attained of participants regarding EI and online teaching self-efficacy. The Implications for enhancing EI and online teaching self-efficacy are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".