The relationship between emotional intelligence, previous caring experience and successful completion of a pre‐registration nursing/midwifery degree
Bibliographic record
Abstract
AIM: To examine the relationship between baseline emotional intelligence and prior caring experience with completion of pre-registration nurse and midwifery education. BACKGROUND: Selection and retention of nursing students is a global challenge. Emotional intelligence is well-conceptualized, measurable and an intuitive prerequisite to nursing values and so might be a useful selection criterion. Previous caring experience may also be associated with successful completion of nurse training. DESIGN: Prospective longitudinal study. METHOD: Self-report trait and ability emotional intelligence scores were obtained from 876 student nurses from two Scottish Universities before they began training in 2013. Data on previous caring experience were recorded. Relationships between these metrics and successful completion of the course were calculated in SPSS version 23. RESULTS: Nurses completing their programme scored significantly higher on trait emotional intelligence than those that did not complete their programme. Nurses completing their programme also scored significantly higher on social connection scores than those that did not. There was no relationship between "ability" emotional intelligence and completion. Previous caring experience was not statistically significantly related to completion. CONCLUSION: Students with higher baseline trait emotional intelligence scores were statistically more likely to complete training than those with lower scores. This relationship also held using "Social connection" scores. At best, previous caring experience made no difference to students' chances of completing training. Caution is urged when interpreting these results because the headline findings mask considerable heterogeneity. Neither previous caring experience or global emotional intelligence measures should be used in isolation to recruit nurses.
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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 | 0.002 | 0.010 |
| 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) | 0.004 | 0.001 |
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".