Assessment of knowledge and perceptions toward generic medicines among basic science undergraduate medical students at Aruba
Bibliographic record
Abstract
OBJECTIVE: Use of generic medicines is important to reduce rising health-care costs. Proper knowledge and perception of medical students and doctors toward generic medicines are important. Xavier University School of Medicine in Aruba admits students from the United States, Canada, and other countries to the undergraduate medical (MD) program. The present study was conducted to study the knowledge and perception about generic medicines among basic science MD students. MATERIALS AND METHODS: -test to compare the total score for dichotomous variables, and analysis of variance for others were used for statistical analysis. RESULTS: Fifty-six of the 85 students (65.8%) participated. Around 55% of respondents were between 20 and 25 years of age and of American nationality. Only three respondents (5.3%) provided the correct value of the regulatory bioequivalence limits. The mean total score was 43.41 (maximum 60). There was no significant difference in scores among subgroups. CONCLUSIONS: There was a significant knowledge gap with regard to the regulatory bioequivalence limits for generic medicines. Respondents' level of knowledge about other aspects of generic medicines was good but could be improved. Studies among clinical students in the institution and in other Caribbean medical schools are required. Deficiencies were noted and we have strengthened learning about generic medicines during the basic science years.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".