Emotional-Social Intelligence in Health Science Students and its Relation to Leadership, Caring and Moral Judgment
Bibliographic record
Abstract
The purposes of this study were to describe and compare the emotional-social intelligence (ESI) of students in nursing, physical therapy and health science programs, and to determine the relationship between ESI and each of leadership, caring and moral judgment. Subjects were 154 students from nursing, physical therapy and bachelor of health science (BHSc) programs in a Canadian university and a physical therapy program in an American college. Data were collected by means of self-report measures of ESI, leadership, caring, and moral judgment. The measures included the Bar-On Emotional Quotient Inventory Short (EQ-i:S), the Self-Assessment Leadership Inventory (SALI), the Caring Ability Inventory (CAI), the Caring Dimensions Inventory - 35 (CDI-35) [for nursing only] and the Defining Issues Test (DIT-2) [for physical therapy and BHSc only]. One-way analyses of variance (ANOVA ) revealed no differences between groups for the EQ-i:S, SALI, or DIT-2. There were significant differences for the Courage subscale of the CAI between students in the American physical therapy program and in the Canadian nursing program (p=.025). Pearson correlation coefficients were significant for EQ-i:S and each of SALI (r=.53), CAI-Knowledge (r=.59) and CAI-Courage (r=.60). The EQ-i:S scores were not related to the CDI (r=.15) or the DIT-2 (r=-.06). The results of this study confirmed the positive relationship between ESI and leadership and suggested that ESI may be an important construct in caring. There were no major differences between students in different health science programs, and ESI was not related to moral judgment.
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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 | 0.007 |
| 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 | 0.000 |
| 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".