Cognitive function and shift work among healthcare professionals in India: Findings from the SNORE study
Bibliographic record
Abstract
Abstract Background The SNORE ( S leep D eprivation among N ight shift health staff O n R otation- E valuation) study evaluated the effects of sleep deprivation on healthcare professionals working rotational night shifts. Given the association between sleep deprivation and cognitive impairment, this sub-study examined the prevalence of cognitive impairment and its relationship with sleep deprivation in a tertiary care hospital. Methods A cross-sectional study included 293 healthcare workers (doctors, nurses, paramedics). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) during day and night shifts. Sleep deprivation was evaluated via the Epworth Sleepiness Scale (EPSS) and self-reported sleep hours. Statistical analysis employed the Wilcoxon signed-rank test for shift-wise cognitive score comparison and multivariable logistic regression to explore the relationship between sleep deprivation and cognitive impairment. Results Cognitive impairment prevalence during night shifts was 23.5%, with nurses showing slightly higher rates (26.2%). Cognitive scores were significantly lower during night shifts (median 28) compared to day shifts (median 29). Sleep deprivation was significantly associated with cognitive impairment (adjusted odds ratio [aOR]: 1.78 [1.01–3.16] for EPSS; 9.20 [2.16–39.23] for self-reported sleep hours). Male participants exhibited higher odds of impairment than females (aOR: 2.30 [1.19–4.47]). Conclusion Sleep deprivation significantly impacts cognitive function in healthcare workers, with more pronounced effects during night shifts. These findings underscore the urgent need for interventions promoting sleep health and optimizing working conditions in healthcare settings.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".