Lifestyle behaviors among Canadian nurses working night shifts in the COVID-19 era: a pilot study.
Bibliographic record
Abstract
Abstract: Working night shifts has been associated with negative mental health consequences such as depression, anxiety, and sleep problems. The objectives of this study were to determine the lifestyle behaviors (prevalence of nicotine, caffeine, cannabis, sleep-promoting medication, and alcohol use) and the association between job stress, sleep quality, anxiety, and depression among registered nurses working night shifts in the Canadian province of Saskatchewan in the COVID-19 era. Twenty-two registered nurses ranging from ages 25-65 that work permanent or rotating night shifts participated in an online survey from April 11th to July 15th, 2022. The results showed a strong positive association between sleep disturbance, and depression r (19) = 0.50, [p = 0.029, 95% CI, 0.06, 0.78]. A positive correlation was found between higher levels of reported anxiety and sleep disturbance r (19) = 0.69, [p = 0.001, 95% CI, 0.34, 0.87]. There was a positive correlation between depression and occupational exhaustion r (17) = 0.56, [p = 0.021, 95% CI, 0.10, 0.82]. Anxiety was significantly related to occupational exhaustion r (17) = 0.65, [p = 0.005, 95% CI, 0.24, 0.86] and depersonalization r (17) = 0.52, [p = 0.005, 95% CI, 0.06, 0.80], but not significantly related to personal accomplishment r (17) = -0.34, [p = 0.185, 95% CI, -0.70, 0.17]. In conclusion, a sample of Canadian nurses working night shifts in the province of Saskatchewan during the COVID-19 pandemic showed a significant positive relationship among sleep disturbance, anxiety, and depression. Furthermore, most nurses reported using at least one or more of the following substances: sleep-promoting medication, nicotine, alcohol, and cannabis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".