Health status, resilience and quality of life of first and fourth year nursing students
Bibliographic record
Abstract
Objective: To compare the health status (stress, depressive symptoms and sleep quality), the resilience and quality of life in first and fourth year nursing students.Methods: This is a cross-sectional research conducted in 2016 with 86 students enrolled in first and fourth years of the nursing degree. We applied the instrument for Assessment of Stress in Nursing Students, the Center for Epidemiologic Studies Depression Scale, Pittsburg Sleep Quality Index, Wagnild and Young’s Resilience Scale; and the WHOQOL-BREF. ANOVA (Test F) was applied for data analysis.Results and conclusions: A total of 49 first-year and 37 fourth-year students were sampled for this study. Fourth- year nursing students showed higher levels of stress, lower intensity of depressive symptoms and higher quality of life and resilience levels. The poor sleep quality was prevalent in both groups. Conclusion: although the nursing education potentially contributes for students’ sickness, the experiences lived in this period may strength the resilience skills.Conclusions: Video indexing and retrieval are accomplished by using hashing and $k$-d tree methods, while visual signatures containing color, shape and texture information are estimated for the key-frames, by using image and frequency domain techniques. Experimental results with the dataset of a multimedia information system especially developed for managing television broadcast archives demonstrate that our approach works efficiently, retrieving videos in 0.16 seconds on average and achieving recall, precision and F1 measure values, as high as 0.76, 0.97 and 0.86 respectively.
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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 | 0.001 |
| 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".