Association between gastrointestinal symptoms and insomnia among healthcare workers: a cross-sectional study
Bibliographic record
Abstract
Healthcare workers frequently encounter demanding schedules, shift work, and significant psychological stressors, all of which can contribute to gastrointestinal disturbances and sleep irregularities. Although research has established that stress and shift work are independent risk factors for gastrointestinal discomfort and insomnia, the direct link between these two conditions in healthcare workers remains insufficiently explored. In this cross-sectional study, we recruited 372 healthcare workers from 6 public hospitals in Quanzhou via an online survey. Gastrointestinal and insomnia symptoms were measured using the Gastrointestinal Symptom Rating Scale (GSRS) and the Insomnia Severity Index (ISI), respectively, and analyzed via univariate and multivariate logistic regression models. Overall, 40.6% of participants reported insomnia symptoms. The GSRS total score was positively associated with insomnia both before (OR 1.18, 95% CI 1.12-1.25) and after adjustment for confounders including age, gender, anxiety, depression, somatization, and weekly night shift count (adjusted OR 1.07, 95% CI 1.00-1.15). These findings underscore a significant relationship between gastrointestinal symptoms and insomnia among healthcare workers, highlighting the need for targeted interventions to improve both GI health and sleep quality, thereby enhancing work efficiency and the overall quality of patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".