Demographic and academic characteristics that contribute to burnout occurrence in nursing students-Analytic study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: Several features, such as workload, irregular practice of sports, and work experiences may contribute to the Burnout However, although different investigations have assessed the associations between demographic and academic characteristics and Burnout across different countries, few studies were conducted in Brazil, especially with nursing students. So, we assessed the association of demographic and academic variables to Burnout occurrence in nursing students.Methods: This is a quantitative, analytical and cross-sectional study. We applied a Form to demographic and academic characterization and the Maslach Burnout Inventory in 570 nursing students between April 2011 and March 2012. To compare the occurrence of Burnout and of its subscales regarding to sociodemographic and academic variables, we used the Chi-Square test and the Fisher exact test (Tables 2 × 2), p < .05. The Ethics Research Committee at the University approved this project under protocol No. 0380.0.243.000-10.Results: Burnout occurrence is higher among students enrolled in first semester, who attend 10 disciplines, without thoughts of leaving the course and who has no job activity. The high Emotional Exhaustion and low Professional Efficacy predominate among unemployed students, and who never thought in leaving the course. The high Cynicism predominated among students aged 20-24 years, enrolled in first semester, who does not work and without experience in healthcare.Conclusions: Few demographic and academic characteristics contribute to Burnout occurrence in nursing students, raising the need of interventions to relieve stress in this population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it