Do health care institutions value research? A mixed methods study of barriers and facilitators to methodological rigor in pediatric randomized trials
Bibliographic record
Abstract
BACKGROUND: Pediatric randomized controlled trials (RCTs) are susceptible to a high risk of bias. We examined the barriers and facilitators that pediatric trialists face in the design and conduct of unbiased trials. METHODS: We used a mixed methods design, with semi-structured interviews building upon the results of a quantitative survey. We surveyed Canadian (n=253) and international (n=600) pediatric trialists regarding their knowledge and awareness of bias and their perceived barriers and facilitators in conducting clinical trials. We then interviewed 13 participants from different subspecialties and geographic locations to gain a more detailed description of how their experiences and attitudes towards research interacted with trial design and conduct. RESULTS: The survey response rate was 23.0% (186/807). 68.1% of respondents agreed that bias is a problem in pediatric RCTs and 72.0% felt that there is sufficient evidence to support changing some aspects of how trials are conducted. Knowledge related to bias was variable, with inconsistent awareness of study design features that may introduce bias into a study. Interview participants highlighted a lack of formal training in research methods, a negative research culture, and the pragmatics of trial conduct as barriers. Facilitators included contact with knowledgeable and supportive colleagues and infrastructure for research. CONCLUSIONS: A lack of awareness of bias and negative attitudes towards research present significant barriers in terms of conducting methodologically rigorous pediatric RCTs. Knowledge translation efforts must focus on these issues to ensure the relevance and validity of trial results.
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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 | 0.666 | 0.777 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".