Bibliographic record
Abstract
This study has been conducted to examine and determine the impact of internal evaluation on education quality of nursing group in Zabol University of Medical Sciences, Iran.This is descriptive study with correlational approach.Statistical population of this study consists of all members of nursing college (students, faculty members, staffs) that their number is equal to 200 members and statistical sample size obtained to 132 members based on Cochrane formula that has been chosen using simple random sampling method.Simple random sampling method was applied in this research to select members of nursing college (students, faculty members, staffs).Measurement instrument was a researcher-made questionnaire that consists of 35 questions in which, factors affecting education quality of nursing group of Nursing College of Zabol University of Medical Sciences have been examined.To determine validity and reliability of questionnaire, internal evaluation questionnaires were evaluated based on the analysis of a 30-member sample through SPSS software under the supervision of professional professors and Cronbach's alpha obtained to 0.89.Pearson Correlation Coefficient and regression were used for data analysis.Results obtained from data analysis show that there is a significant relationship between components of internal evaluation (scientific cooperation, objectives, creativity, program and bylaw, academic activities, information technology, and faculty members) and quality of education and of these components, objectives and creativity have the highest and lowest effect on quality of education, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.714 | 0.535 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".