Assessment of Hafr Albatin University female workers' knowledge and health related behaviors regarding osteoporosis
Bibliographic record
Abstract
Background and aim: Osteoporosis is a global and preventable public health problem considered as a potentially life threatening condition and having negative effects on both health and economics. The aim of this study was the assessment of Hafr Albatin University female workers' knowledge and health related behaviors regarding osteoporosis.Methods: A descriptive exploratory research design was implemented. Setting: The study was conducted in a college. The sample was of convenience. There were 352 women working at the college at the time of data collection. Tools: self-administrative questionnaire consisted of three parts: Part I: sociodemographic data, Part II: women's osteoporosis health related behaviors. Part III: knowledge related to osteoporosis.Results: About 50.9% of the participants had moderate level of overall knowledge about osteoporosis. Only 13.7% of the participants reported the source of their knowledge was a physician. Regarding osteoporosis related health behaviors, less than one third (29.7%) of the participants were consuming foods rich in calcium, but the majority of them consumed this food only 1-2 time per week and 30.3% of them were exposed to sunlight daily. 58.2% of the participants were drinking soda drinks daily and 83.4% were having coffee frequently.Conclusions: Women had moderate level of overall knowledge about osteoporosis and there was a gap between knowledge and applying the related health behaviors. Where the women had information about the health and risk related behaviors but not follow this information in correct and accurate way. Recommendation: Prevention programmes should be designed to increase awareness of working women about osteoporosis. screening, prevention and treatment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".