Perceived impact of a one-week journalology training course on scientific reporting competencies: prospective survey
Bibliographic record
Abstract
INTRODUCTION: The debate on scientific research and reporting integrity issues in Brazil is incipient. Literature suggests that a journalology training course could help to improve the competencies of the participants. OBJECTIVE: To evaluate the immediate impact of a journalology training course on perceived academic competencies, comprised of knowledge, attitudes, and skills. METHODS: The course was taught in 5 consecutive days to an online audience of individuals from the health sciences. A self-applied questionnaire was employed before and immediately after the course, which included initial and acquired perceived knowledge, attitudes, skills. The Wilcoxon non-parametric test for paired samples was used for analysis. RESULTS: A total of 45 individuals participated in the course, with a 53% response rate before and after. The number of participants in each course session ranged between 32 and 45. There was an improvement in perceived knowledge of: (1) writing review articles; (2) ethical aspects of research; (3) scientific authorship; (4) predatory practices; (5) publication bias and spin, and (6) researcher evaluation. There was no improvement in self-reported attitudes towards any item. There was an improvement in the perception of skills relating to: (1) writing a response letter and (2) writing an opinion as a reviewer. CONCLUSIONS: Overall, attendees who participated in the survey reported perceived improved knowledge and skills in some items but not in their attitudes. Therefore, the course appears to have been unable to modify perceived scientific reporting competencies.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.005 | 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".