Investigating Knowledge and Attitude of Nursing Students Towards Iranian Traditional Medicine-Case Study: Universities of Tehran in 2012-2013
Bibliographic record
Abstract
The present study aimed at Investigating the knowledge and attitude of Nursing Students towards Iranian Traditional Medicine in universities of Tehran in 2012-2013. 300 students of nursing studying at different universities in Tehran participated in this descriptive, cross-sectional study. The data was collected through a standard questionnaire with an acceptable validity and reliability. The questionnaire was made of five sections including demographic, general knowledge of the Iranian traditional medicine, general attitude towards it, resources of the Iranian traditional medicine and the barriers to it. The results revealed that general knowledge of the students about Iranian traditional medicine and complementary medicine is low. The attitude of the students towards including Iranian traditional medicine and complementary medicine in their curriculum is positive. General attitude of students towards Iranian traditional medicine is positive too. The majority of the participants had not passed any course on Iranian traditional medicine. There was no relationship between participants' attitude towards Iranian traditional medicine and the number of semesters they had passed. Considering the participants' positive attitude and their low level of knowledge, it seems necessary for the university policy makers to provide nursing students with different training courses on Iranian traditional medicine and complementary medicine in order to increase their knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".