Factors relating to nonpublication and publication bias in clinical trials in Canada: A qualitative interview study
Bibliographic record
Abstract
AIMS: This study aims to understand factors contributing to nonpublication and publication bias in clinical trials in Canada. METHODS: Qualitative interviews were conducted between March 2019 and April 2021 with 34 participants from the Canadian provinces of Alberta, British Columbia and Ontario, including 17 clinical trial investigators, 1 clinical research coordinator, 3 research administrators, 3 research ethics board members and 10 clinical trial participants. We conducted a thematic analysis involving coding of interview transcripts and memo-writing to identify key themes. RESULTS: Several factors contribute to nonpublication and publication bias in clinical trial research. A core theme was that reporting practices are shaped by incentives within the research system taht favour publication of positive over negative trials. Investigators are discouraged from reporting by experiences or perceptions of difficulty in publishing negative findings but rewarded for publishing positive findings in various ways. Trial investigators more strongly associated positive clinical trials than negative trials with opportunities for industry and nonindustry funding and with academic promotion, bonuses and recognition. Research institutions and ethics boards tended to lack well-resourced, proactive policies and practices to ensure trial findings are reported in registries or journals. CONCLUSION: Clinical trial reporting practices in Canada are shaped by incentives favouring reporting of positive over negative trials, such as funding opportunities and academic promotion, bonuses and recognition. Research institutions could help change incentives by adopting performance metrics that emphasize full reporting of results in journals or registries.
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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 | 0.080 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".