Physical Activity Status and Position of Governmental Employees in Changing Stage Based on the Trans-Theoretical Model in Hamadan, Iran
Bibliographic record
Abstract
Physical inactivity is the fourth leading risk factor for death worldwide. Given the key role of employees as valuable human resources and increasing sedentary life style among them, the aim of this study was to evaluate physical activity status and position of governmental employees in changing stage based on the Trans-Theoretical Model (TTM) in Hamadan, Iran, in 2014.This descriptive-analytical study was performed on 1200 government employees selected using proportional stratified random sampling. Data collection was performed using a three-section questionnaire containing demographic characteristics, SQUASH (Short questionnaire to assess health-enhancing physical activity) questionnaire and Marcus et al's five-part algoritm. Data were analyzed by multiple linear and logistic regression, Chi-square, T-test and ANOVA using SPSS-20. The mean age of the participants was 38.12±8.04 years. About a half of the employees were in the preparatory stage of TTM.49.2% and 50.8% of the sample were classified as active and inactive, respectively .Associations between physical activity status and exercise stage of change were found. The associations between exercise stage of change and age, sex, work experience, education and marital status were significant (p<0.05). Work experience and education were strongly predictors of physical activity (PA) status and accounted for 31.2% of variance in PA (adjusted R2=0.312, R2 change=0.01). The results of this study showed that TTM was useful to evaluate and predict physical activity behavior among the Iranian governmental employees and can be utilized by health planners to inform appropriate intervention strategies, specifically in work place.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".