Covid-19 Pandemisi Sürecinde Yoğun Bakımda Çalışanların İş Yüküyle Tükenmişlik Düzeyi Arasındaki İlişki
Bibliographic record
Abstract
The research is a cross-sectional and descriptive study that examines the relationship between the increased workload in Covid-19 intensive care units and the burnout levels of nurses. The study population consists of nurses working in Covid-19 intensive care units. The sample was composed of nurses who met the inclusion criteria for the study and volunteered to participate in the research at Yıldırım Beyazıt University Training and Research Hospital. Pandemic is generally defined as the worldwide spread of a previously unseen disease. Pandemics have a high rate of infecting and causing fatalities in the global population. Along with pandemics, come various societal issues affecting areas such as work, the economy, education, and, notably, the healthcare sector. The unprecedented Covid-19 pandemic has placed immense pressure on the healthcare sector worldwide, exacerbating pre-existing workloads and burnout issues. In our study addressing this problem, questions were posed using a sociodemographic data form, the Maslach Burnout Inventory, and the Individual Workload Perception Scale. The collected data was analyzed, and the findings were compared with the literature to draw conclusions. Recommendations were made based on these analyses. Key words: Covid-19, Nursing, Workload, Burnout
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".