Bibliographic record
Abstract
Shiftwork is one of health care worker's oldest problems and is known to have important implications on health. Health risks are compounded with age and the amount of cumulated shiftwork. No shift system is clearly advantaged, yet the worker's ability to choose the shift system seems to maximize adaptation to shiftwork. When designing a work schedule, it is important to take into consideration the shift pattern, length of the shift, and the number of consecutive days worked. A poorly designed work schedule can impact the quality of care, the personal and professional outcomes for health care workers, patient satisfaction, length of stay, unplanned absenteeism, cost effectiveness, and productivity. Long-term studies of shiftworkers may disproportionately represent workers who have adapted to shiftwork. Self-scheduling is an interesting alternative in the quest for a more responsive work environment and is a strategy for retention among new, mid-career, and senior nurses. Planned on-site napping may be a useful tool to combat the pernicious effects of sleep debt on performance. Guidelines must be developed and initiatives implemented and evaluated to protect health care workers, especially older female shiftworkers, from the negative impact of shiftwork as they represent a precious resource in a shrinking supply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.234 | 0.109 |
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".