Burnout among Health Care Workers during COVID-19: An Applied Study at King Abdulaziz Hospital in Al Ahsa
Bibliographic record
Abstract
The objective of this study was to measure burnout prevalence among health care workers at KAH during the COVID-19 pandemic. The method applied in this study was a descriptive quantitative approach. We collected the data via self-assisted online survey utilizing the Copenhagen Burnout Inventory (CBI), a reliable instrument to measure burnout by investigating its three subdomains: personal, work-related and client-related. The results came out from this study were that: 244 HCWs completed the questionnaire. The mean of total burnout, personal burnout, work-related burnout, and client-related burnout score was 55.89 (SD 19.8), 64.8 (SD 22.16), 57.6 (SD 21.05) and 45.18 (SD 24.54), respectively. Factors that contributed to the increased levels of burnout included: younger age, female gender, the nursing profession, fewer years of experience, extended working hours per shift, fewer off days per month, fewer hours of sleep per night, increased workload, prolonged contact with COVID-19 cases, frequent change in regular job duties and the higher perceived psychological impact of the pandemic. The study concluded that healthcare workers at KAH experienced high rates of personal burnout and work-related burnout during the COVID-19 pandemic. Therefore, institutional intervention to address burnout was deemed necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".