Investigating Level of Knowledge, Attitudes and Practices of Health Personnel in Larestan regarding Andropause in 2010
Bibliographic record
Abstract
Background and Aim: Andropause in men refers to a condition similar to menopause in women which is associated with decreased level of testosterone and Leydig cells. The results of research have shown that there is still low level of awareness and attitudes towards Andropause among health professionals. Therefore, this study aimed at assessing the level of knowledge, attitudes and performance of Larestan health care personnel regarding Andropause in 2010.Materials and methods: This study is descriptive analytic. Sampling was done by census method. Data gathering tool was a researcher-made questionnaire for assessing knowledge, attitude and practice of nursing and medical staff regarding Andropause. Using SPSS 16. Descriptive statistics and t-test were used for data analysis. Results: The mean scores of knowledge, attitudes and practice in relation to Andropause among nursing staff were significantly lower in contrast with practitioners. Comparison of the mean scores for knowledge and practice in the two groups was statistically significant difference (P <0.001). But there was not a statistically significant difference the attitude scores (P =0.84).Conclusions: Given the importance of men's health, lack of knowledge, attitude and stated practice about Andropause among members of the health care teams, it seems essential in teaching about it in academic programs.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".