Effectiveness of Interventions on Habits Among University Staff in Southeastern Iran
Bibliographic record
Abstract
Background: Health-promoting environments are a key focus of the Ottawa Strategic Charter, which aims to enhance health in the workplace and reduce unhealthy behaviors among employees. Objectives: This study aimed to evaluate the effectiveness of health-promoting interventions on the habits of university staff in Southeastern Iran. Methods: This semi-experimental study was conducted from 2020 to 2021 in Zahedan, Southeastern Iran. A total of 254 administrative staff from the University of Medical Sciences and Sistan & Balochistan University were selected using available randomized methods and divided into control and intervention groups. The study instrument was a researcher-designed questionnaire, which demonstrated a content validity ratio (CVR) of 0.8, a Content Validity Index (CVI) ranging from 0.83 to 0.97, and an internal consistency with a Cronbach's alpha of 0.85. The questionnaire was distributed online via the Porsline platform. For the intervention, training sessions were conducted online, and the session files and training booklet were made available to intervention group staff through the university's education system. A post-test was administered one month after the pretest. Data were analyzed using SPSS 21 software, with pretest results adjusted using chi-square and ANCOVA statistical tests. Results: The study found that the mean age of participants was 40.5 ± 7.4 years. Scores for employee knowledge, attitude, and behavior were 80.0 ± 16.4, 86.6 ± 22.8, and 80.8 ± 17.6, respectively. The most significant factors influencing attitudes were education (P < 0.001) and health status (P = 0.001). Type of employment (P = 0.025), work experience (P = 0.017), and health status (P = 0.037) were also significantly associated with behavior. After adjusting for pretest results, there were significant differences between the intervention and control groups in terms of scores for knowledge, attitude, and behavior (P < 0.001). Conclusions: The study concluded that employees with correct attitudes and healthy behaviors reported better health outcomes. Additionally, educational interventions effectively improved staff knowledge, attitudes, and behavior. To enhance health status, reduce employee disability, decrease absenteeism, and increase organizational productivity, it is recommended to plan and implement workplace health promotion programs focused on employees.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".