Meeting the Canadian strength training recommendations: Implications for the cardiometabolic, psychological and musculoskeletal health of nurses
Bibliographic record
Abstract
AIM: To examine the proportion of nurses meeting the strength training recommendation and its associated cardiometabolic, psychological and musculoskeletal benefits. BACKGROUND: Strength training targets poor physical and mental health often reported by nurses; however, it is unknown whether nurses are meeting the strength training guidelines. METHODS: Nurses from 14 hospitals completed a 7-day physical activity log. Nurses were considered meeting the recommendation if they reported ≥2 strength training sessions per week. Cardiometabolic, psychological and musculoskeletal health, and levels of motivation were compared between nurses meeting and not meeting the guidelines. RESULTS: , p = .007) and waist circumference (73.8 ± 8.3 vs. 81.1 ± 11.7 cm, p = .017); and higher vigour-activity (18.0 ± 5.8 vs. 15.6 ± 6.5 points, p = .046) and self-determined motivation (relative autonomic index: 54.9 ± 20.3 vs. 45.0 ± 23.8 points, p = .042) scores than nurses not meeting the recommendation. CONCLUSION: While the proportion of nurses meeting the strength training recommendation was small (<10%), they had lower body mass and waist circumference, and higher vigour-activity. IMPLICATIONS FOR NURSING MANAGEMENT: Strategies to increase the strength training engagement may improve the cardiometabolic health and increase vigour among nurses.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".