Effectiveness of Some Educational Methods and Tools on Improving the Level of Understanding of Biostatistics among Medical Students and Paramedical Postgraduate Students
Bibliographic record
Abstract
It has been observed that medical students and researchers lack sufficient knowledge in understanding statistical concepts. This indicates the importance of improving the level of instruction in this field. This experimental study was conducted to introduce and investigate the effectiveness of some educational methods and tools in improving the level of understanding of biostatistics among medical students and paramedical postgraduate students. For this purpose, from 40 medical students and 20 paramedical postgraduate students, who attended the biostatistics course, pre-test and post-test questionnaires were collected. The medical students were divided into two training groups, namely, training with the help of software (intervention group), and the traditional (lecture method) group. The paramedical postgraduate students were also divided into two groups, except that for the intervention group, in addition to training with the help of software, educational DVDs were also provided. Knowledge, attitude and the awareness index of the students were determined by using a questionnaire. Post-test results indicate that, the awareness index in the intervention group was significantly higher than the control group (P<0.05). The new method of teaching significantly upgraded the knowledge of the students (P<0.05) and increased the level of attitude of the medical students (P<0.04). Comparing the post-test results of the two groups, i.e., medical students and paramedical postgraduate students, demonstrated that a combination of software and instructional DVDs had a positive effect on the desired outcome (P<0.01). Usage of statistical software and additional virtual methods will contribute to increasing the level of knowledge and attitude of the students toward biostatistics. The training method and, accordingly, the curricula of biostatistics courses in medical and paramedical schools must be revised.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".