Bibliographic record
Abstract
PURPOSE OF REVIEW: This review focuses on recent literature examining and targeting the physical activity and sedentary behaviour of nurses. The role of physical activity and sedentary behaviour in preventing and managing cardiovascular disease (CVD) in women is also discussed. RECENT FINDINGS: Nurses (most of whom are women) represent the largest professional group within the health care workforce and many present with risk factors for CVD (e.g. physical inactivity, sedentary behaviour, overweight/obesity, hypertension, dyslipidemia, diabetes, smoking, depression, anxiety). Several studies have measured the physical activity and sedentary behaviour of nurses and found low levels of physical activity (i.e. most do not meet physical activity guidelines) and high levels of sedentary behaviour (50-60% of the day). Nurses working rotating shifts, 12-h shifts and/or working full-time or part-time (vs. casual) may be at greater risk of physical inactivity; however, the opposite has been observed for sedentary behaviour. Few interventions targeting nurses' physical activity levels have shown promise, but those that have used activity monitors with behavioural strategies; no studies, to date, have evaluated the impact of sedentary behaviour interventions in nurses. SUMMARY: Improving the physical activity levels and reducing the sedentary behaviour of nurses is important for nurses' cardiovascular health. There is a need for interventions to address low physical activity and high sedentary behaviour among nurses.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".