How do the relationships among health phenomena explain the nursing students’ quality of life?
Bibliographic record
Abstract
Background and objective: To know the direct relationships between stress, sleep quality, depressive symptoms, resiliency, and quality of life of nursing students. Less is known about how the simultaneous relationships between these variables may explain the nursing students’ quality of life remains unclear. We assessed how the simultaneous causal relationships among stress, depressive symptoms, sleep quality, and resilience explain the nursing students’ quality of life one year after starting a nursing degree program.Methods: This was a one-year longitudinal study. Data were gathered with validated tools from first university-year nursing students enrolled in two public Brazilian universities at the beginning (n = 117) and end (n = 100) of March 2016. The latent variable analysis- a complement of the R statistical package- was used to estimate the Structural Equation Modelling.Results: The final model showed good fitness and residues quality. Stress decreased sleep quality and increased the intensity of the depressive symptoms. Both of these, directly and indirectly, reduced the quality of life. Resiliency decreased stress levels and depressive symptoms and improved sleep quality.Conclusions: The academic environment has the potential for illnesses, impacting the quality of life. On other hand, resiliency plays a protective role on nursing students by reducing stress and its negative effects. Education institutions need to rethink their curricular elements, promote resilience and create actions to promote students’ health.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".