Incorporation of Responsible Conduct of Research Education in Brazilian Graduate Pharmacy Programs: A Qualitative Assessment
Bibliographic record
Abstract
OBJECTIVE: Education in Responsible Conduct of Research (RCR) is essential for fostering a culture of research integrity. This study identifies the extent and content of RCR education in Brazilian graduate programs in Pharmacy. METHODS: Brazilian graduate programs in Pharmacy offering RCR curricula were identified through a national database of graduate programs. Course duration, mandatory status, and syllabi were extracted for analysis. Syllabi were analyzed deductively, based on the National Institutes of Health's recommended RCR core content. Content not aligned with the National Institutes of Health´s recommendations was categorized inductively. RESULTS: Out of the 69 graduate programs, 71% (49/69) offered at least one course that included RCR content in their syllabi. Overall, 62.3% (43/69) included RCR content as part of a broader course (integrated courses), whereas 26.1% (18/69) offered courses solely focused on RCR (dedicated courses). Dedicated and integrated courses were required by 4.3% (3/69) and 13.0% (9/69) of programs, respectively. Most programs' courses had a duration of 30 h. Ethical standards for research regarding animals were the most common content offered, followed by research regarding human beings. Most courses that addressed misconduct focused on plagiarism. CONCLUSION: Most Brazilian graduate programs in Pharmacy include some RCR-related content, primarily within integrated and elective courses. We recognize that the implementation of dedicated mandatory courses is needed. Brazilian funding agencies have the potential to foster the expansion of research integrity by requiring such courses across all graduate programs. In turn, it would encourage institutions to expand educational initiatives and help to consolidate a strong culture of research integrity.
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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 | 0.044 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".