Effects of Sleep-Deprived Lifestyle on Cognitive Abilities of Healthcare Professionals Working in Shifts
Bibliographic record
Abstract
The objective of this study was to assess the impacts of sleep deprivation resulting from night shift employment on the general health and quality of life of nurses.A total of 58 healthcare professionals were recruited for this investigation.The Stanford Sleepiness Scale (SSS), Epworth Sleepiness Scale (ESS), Beck Depression Inventory (BDI) and Pittsburgh Sleep Quality Index (PSQI) questionnaires were completed by them.The Montreal Cognitive Assessment (MoCA), Vigilance test, and Stroop test were utilized to evaluate the impact of sleep deprivation on the subjects' cognitive performance, the Vigilance test, and the Stroop test.The individuals were divided into three groups: Severe sleep deprivation was defined as getting less than 4 h of sleep each night (SD); person was classified as mildly to moderately sleep deprived (MD) if they slept for 4-6 h per night, and as non-sleep deprived (NonD) if they slept for more than 6 h per night.Among shift-workers in the healthcare industry, 71% claimed they had trouble sleeping.The SD group exhibited a greater ESS than the MD and NonD groups.Compared to their daytime MocA score of 27.92, 67% of nurses' nighttime MocA score was 24.62.Over the course of the evening, 33% committed more math errors.It was discovered that 72%, 84%, and 67% of nurses performed worse on the nighttime Stroop's color, alertness, and memory tests.The cognitive impairment of shift-work nurses was therefore statistically significant.These findings demonstrated that medical professionals' well-being, productivity, and ability to handle stress may all suffer from sleep deprivation, which may have an effect on the caliber of treatment given to patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".