Use of pedometer-driven walking to promote physical activity and improve health-related quality of life among meat processing workers: a feasibility trial
Bibliographic record
Abstract
BACKGROUND: Current evidence supports the use of pedometers as effective motivational tools to promote physical activity and improve health-related quality of life in the general population. The aims of this study are to examine whether a pedometer-driven walking programme can improve health-related quality of life, and increase ambulatory activity in a population of meat processing workers when compared to a control group receiving educational material alone. METHODS/DESIGN: A feasibility study employing a randomized controlled trial (RCT) design will collect data at three time points. A sample of up to 60 meat workers will be recruited and randomly assigned to either an intervention group N = 30 (12-week pedometer-driven walking program, brief intervention, and educational material), or control group N = 30 (educational material only). The primary outcomes of ambulatory activity, health-related quality of life, and functional capacity, will be evaluated at baseline, immediately following the 12-week intervention and then at three month post-intervention. DISCUSSION: This paper describes the design of a feasibility randomized controlled trial, which aims to assess the effectiveness of the introduction of a workplace pedometer-driven walking program compared to normal lifestyle advice in meat processing workers. TRIAL REGISTRATION NUMBER (ANZCTR): 12613000087752.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".