Changes in Emotional-Social Intelligence, Caring, Leadership and Moral Judgment during Health Science Education Programs
Bibliographic record
Abstract
In addition to having academic knowledge and clinical skills, health professionals need to be caring, ethical practitioners able to understand the emotional concerns of their patients and to effect change. The purpose of this study was to determine whether emotional-social intelligence, caring, leadership and moral judgment of health science students change from the beginning to the end of their programs. Students from nursing, bachelor of health science and two physical therapy programs completed self-report questionnaires to evaluate emotional-social intelligence [BarOn Emotional Quotient Inventory: Short (EQ-i:S)], caring [Caring Ability Inventory (CAI)] and leadership [Self-Assessment Leadership Inventory] at the beginning and end of their programs. Students in three of the programs also completed a test of moral decision-making [Defining Issues Test (DIT-2)] at both time points. Two-way analyses of variance (program versus time) demonstrated significant time effects for the total score of EQ-i:S, the Knowing subscale of CAI and the N2 score of the DIT-2. There were no major differences between programs. It can be concluded that health science students show small improvements in emotional-social intelligence, caring and moral judgment from the beginning to the end of their educational programs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".