Computer Literacy Among Students of Zahedan University of Medical Sciences
Bibliographic record
Abstract
INTRODUCTION: The need for medical students to be computer literate is vital. With the rapid integration of information technology (IT) in the health care field, equipping students of medical universities withcomputer competencies to effectively use are needed. The purpose of this study was to assess computer literacy (CL) needs of medical sciences students. METHODS: This is descriptive-analytic. The population of the study comprised all students at Zahedan University of Medical Sciences. 385 students from allschools (Medicine, dentistry, paramedics, health, rehabilitation, nursing and midwifery) were selected through randomized- classified sampling. For data collecting, the Lin Tung- Cheng questionnaire was used which it contained 24 items in six sections.The obtained data analyzed by SPSS 15. RESULTS: The results showed that the 77.1% had personal computer. The total mean of students' computer literacy around six domains was 141.9±49.5 out of 240. The most familiarity with computers was the ability to it in internet (29.0±11.4) and the lowest was familiarity and using ability of hard ware (17.5±10.6). There was a significant relationship between passing the Computer lesson (P=0.001), passing Computer course (P=0.05) and having personal computer (P=0.001) with the mean of computer literacy. DISCUSSION: In sum, the medical sciences students' familiarity with computer literacy was not satisfactory and they had not appropriate familiarity with computer literacy skills. The researchers suggest the officials and in-charges to plan educational program for improving computer literacy skills in medical sciences students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".