Educational Skills Evaluation among Health Care Providers (Behvarzan) in Educating of Clients Who Referred to the Health Care Homes
Bibliographic record
Abstract
INTRODUCTION: Health care providers (Behvarzan) are the primary health care givers and their educational skills are an important factor to offer safe health care and promote the public health. So that this study was conducted to evaluate the educational skills among health care providers in educating the referrals to health homes. METHODS: This cross-sectional was conducted in health homes of Esfarayen health care network in 2015. By the method of enumeration 81 health care providers were included to the study. Data was collected by the questionnaire of evaluating the educational skills of Behvarz(s) designed by the researcher. Data were analyzed through descriptive statistics and analytical tests such as Pearson's correlation, independent T-test and ANOVA by the SPSS v.20. RESULTS: The results of this study had shown that 45.7% of subjects had good educational skills. Also the statistical calculation showed a significant difference between some variables such as internet usage (p=0.008) and internet usage in workplace (p=0.001) with Behvarz(s) educational skills. CONCLUSION: The educational skills of Behvarz(s) working in health homes was satisfactory. A significant relation was found between educational skills and some other factors. So then planning for upgrading the educational skills of Behvarz(s) and conducting much more studies to find effective factors on educational skills is recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".