Influence of Emotional Quotient on Clinical Capacity of Vocational Nursing Students
Bibliographic record
Abstract
Objective To investigate the current status of emotional quotient(EQ)and clinical capacity of vocational nursing students(VNS),and to analyze the correlation between them.Methods A total of 157 practicing VNS in 2009 were investigated on EQ and clinical capacity with Toronto Alexithymia Scale-20 and clinical competence checklist of VNS.Results The total EQ score for VNS was 2.48±0.36.Their abilities to identify emotions,describe the emotions,and think externally were at the middle level.The total score of clinical capacity was 3.98±0.51.The highest score was observed in the clinical management and the lowest score was in the clinical research capacity.Positive correlation was determined between clinical research capacity and general comments of EQ,inability to describe the emotion(P0.01).And without correlation to external-oriented thinking(P0.05).Positive correlation between inability to describe the emotion and clinical disposition capacity,capacity of communicate care,professional psychological quality, clinical management capacity(P0.05).Conclusion Medical institutions should improve the vocational training target for VNS,enhance their capacities of clinical teaching,health education and clinical research, simulate the real working environment and strengthen the targeted training of nursing students to improve their emotional and clinical capacities.
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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 | 0.000 |
| 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".