Impact of nursing students’ profile on burnout syndrome and hardiness personality
Bibliographic record
Abstract
Objective: The stressful college environment may cause Burnout Syndrome in nursing students, but few of them present stress resistance and do not show Burnout signs. Investigations that simultaneously assess these groups are limited. So, we assessed the impact of nursing students’ profile (biosocial and academic features) on the occurrence of Burnout Syndrome and Hardiness Personality.Methods: Cross-sectional, analytic and quantitative study. We applied a biosocial and academic form, the Maslach Burnout Inventory and the Hardiness Scale in 570 Brazilian nursing students. Logistic and linear regression analysis were used to assess the impact of biosocial and academic features on Burnout and Hardiness. The Ethics Research Committee at the University approved this project under protocol No. 0380.0.243.000-10.Results: Interest of keeping enrolled in course, sedentary lifestyle, semester and number of disciplines taken by students significantly contributed to increase the Burnout scores. Age, absence of children, living with family, dissatisfaction with nursing course and the unemployment significantly increased Hardiness scores. The variable “academic load” contributed to both phenomena.Conclusions: While biosocial features strength the hardy components in nursing students, protecting them from negative stress outcomes, nursing training characteristics seem negatively impact on student’s health. Thus, identifying the factors that contribute to stress resistance and those that may increase the risk of Burnout, will support interventions that to promote Hardy personality and prevent Burnout in academic environment.
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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 | 0.004 |
| 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 | 0.000 | 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".