Lessons from the COVID-19 pandemic and recent developments on the communication of clinical trials, publishing practices, and research integrity: in conversation with Dr. David Moher
Bibliographic record
Abstract
Abstract Background The torrent of research during the coronavirus (COVID-19) pandemic has exposed the persistent challenges with reporting trials, open science practices, and scholarship in academia. These real-world examples provide unique learning opportunities for research methodologists and clinical epidemiologists-in-training. Dr. David Moher, a recognized expert on the science of research reporting and one of the founders of the Consolidated Standards of Reporting Trials (CONSORT) statement, was a guest speaker for the 2021 Hooker Distinguished Visiting Professor Lecture series at McMaster University and shared his insights about these issues. Main text This paper covers a discussion on the influence of reporting guidelines on trials and issues with the use of CONSORT as a measure of quality. Dr. Moher also addresses how the overwhelming body of COVID-19 research reflects the “publish or perish” paradigm in academia and why improvement in the reporting of trials requires policy initiatives from research institutions and funding agencies. We also discuss the rise of publication bias and other questionable reporting practices. To combat this, Dr. Moher believes open science and training initiatives led by institutions can foster research integrity, including the trustworthiness of researchers, institutions, and journals, as well as counter threats posed by predatory journals. He highlights how metrics like journal impact factor and quantity of publications also harm research integrity. Dr. Moher also discussed the importance of meta-science, the study of how research is carried out, which can help to evaluate audit and feedback systems and their effect on open science practices. Conclusion Dr. Moher advocates for policy to further improve the reporting of trials and health research. The COVID-19 pandemic has exposed how a lack of open science practices and flawed systems incentivizing researchers to publish can harm research integrity. There is a need for a culture shift in assessing careers and “productivity” in academia, and this requires collaborative top-down and bottom-up approaches.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Reporting · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchScholarly communicationResearch integrity Domain: Reporting · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.256 | 0.461 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.031 | 0.045 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.053 | 0.129 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".