Investigate the anxiety between undergraduate and postgraduate nursing students and its relation to their socio-demographic characteristics
Bibliographic record
Abstract
Objective: Anxiety is a warning sign for an upcoming risk and is a common reaction of most people reacting to stressful events. The purpose of this study was to investigate anxiety between undergraduate and postgraduate students in nursing and its relation to their socio-demographic characteristics.Methods: This is a cross-sectional, quantitative study with a study sample of comprised of n = 177 nursing students, n1 = 121 undergraduate students and n2 = 56 postgraduate students. The collection of the empirical data was carried out using a fully structured and Self-reported questionnaire which recorded their individual characteristics and the State-Trait Anxiety Inventory (STAI). The statistical analysis was conducted using the software package SPSS.Results: State anxiety was not found to differ significantly between undergraduate and postgraduate students. While Trait and Total Anxiety differed significantly, showing higher mean levels for undergraduate students. Moreover, the students of the last two years of study had a higher mean level of State Anxiety compared to the students of the first year of study.Conclusions: The majority of undergraduate and postgraduate students showed relatively low levels of anxiety. Animportant factor to predict anxiety in undergraduate students was the year of study, while for the postgraduate students were age and marital status.
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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 | 0.003 |
| 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".