0647 Impact of Work Schedule on Sleep and Mood in Shift Workers
Bibliographic record
Abstract
Shift work is known to alter sleep quantity and quality. However, the way work schedule affects sleep in the presence of shift work disorder (SWD) is unclear. In addition, it is unknown if mood of shift workers differs according to work schedules. 79 shift workers of which 43 met SWD criteria were recruited. They worked 6 to 10 nights out of 14. Night shifts are either consecutive (CNS), or fragmented (FNS) (nights are intervened with free days). Participants completed sleep diaries for two weeks and self-reported questionnaires. Those without SWD and satisfied with their sleep were good sleepers (GS). Four groups were created: (1)CNS-SWD; (2)CNS-GS; (3)FNS-SWD; and (4)FNS-GS. Total Sleep Time (TST), Total Wake Time (TWT), and Sleep Onset Latency (SOL) were computed for the main sleep episode (longest sleep episode after night shift) and for the 24-hour sleep period (includes all sleep episodes occurring in that period). For main sleep episode, CNS-SWD have higher TWT and SOL and a lower TST than CNS-GS (ps< .001, .05, .02), while FNS-SWD have higher TWT (p< .04) than FNS-GS. For 24-hour sleep period, the same significant differences are observed for TWT and SOL. Depression and anxiety levels are higher for CNS-SWD compared to CNS-GS (ps< .008 and .01, respectively). There is no significant difference between FNS-SWD and FNS-GS for these variables. Fragmented night shift reduces sleep time of shift workers with SWD and GS while continuous night shift reduces sleep only for those with SWD. Workers under consecutive or fragmented night shift present similar wake time. Anxiety and depression levels differ according to the work schedule and SWD. Depression symptoms are more important for continuous night shift workers with SWD. The two distinct work schedules differently impact sleep and mood: The fragmented one impact on sleep time while the continuous one impact on mood in presence of SWD. Study supported by a CIHR funding 191771 awarded to A.V.
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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 | 0.002 |
| 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) | 0.004 | 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".