Impact of a Pedometer Program on Nurses Working in a Health-Promoting Hospital
Bibliographic record
Abstract
The aim of this research was to describe the impact of a pedometer-based activity program on a subset of nurses in a university-affiliated, multisite health care center in Canada. This study used a longitudinal design with preintervention-postintervention (8 weeks) and follow-up (6 months). At baseline, 60 nurses participated; 51 (85%) remained for the postprogram assessment and 33 (55%) also completed the follow-up questionnaire. Data were collected through self-administered questionnaires (weight, height, fatigue, insomnia, stress and step data) and blood tests (total cholesterol and low-density lipoprotein and high-density lipoprotein cholesterol). At postprogram, participants reported 12 thinsp;912 steps on average per day. At follow-up, 79% of participants indicated that they maintained their physical activity after the pedometer program. A significant decrease in insomnia was evident in postprogram scores compared with baseline scores, and this decrease was maintained at follow-up. A significant decrease in minutes spent sitting per week was also observed from baseline to postprogram and also maintained at follow-up. Participants' stress and low-density lipoprotein cholesterol levels decreased from baseline to postprogram (marginally significant). Finally, their weight decreased from baseline to follow-up (marginally significant). The pedometer program generated some positive outcomes for nurses after 6 months.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".