Demographic and academic characteristics that contribute to burnout occurrence in nursing students-Analytic study
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".