Development and Evaluation of a Pedagogical Tool to Improve Understanding of a Quality Checklist: A Randomised Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop and evaluate a pedagogical tool to enhance the understanding of a checklist that evaluates reports of nonpharmacological trials (CLEAR NPT). DESIGN: Paired randomised controlled trial. PARTICIPANTS: Clinicians and systematic reviewers. INTERVENTIONS: We developed an Internet-based computer learning system (ICLS). This pedagogical tool used many examples from published randomised controlled trials to demonstrate the main coding difficulties encountered when using this checklist. Randomised participants received either a specific Web-based training with the ICLS (intervention group) or no specific training. OUTCOME MEASURES: The primary outcome was the rate of correct answers compared to a criterion standard for coding a report of randomised controlled trials with the CLEAR NPT. RESULTS: Between April and June 2006, 78 participants were randomly assigned to receive training with the ICLS (39) or no training (39). Participants trained by the ICLS did not differ from the control group in performance on the CLEAR NPT. The mean paired difference and corresponding 95% confidence interval was 0.5 (-5.1 to 6.1). The rate of correct answers did not differ between the two groups regardless of the CLEAR NPT item. Combining both groups, the rate of correct answers was high or items related to allocation sequence (79.5%), description of the intervention (82.0%), blinding of patients (79.5%), and follow-up schedule (83.3%). The rate of correct answers was low for items related to allocation concealment (46.1%), co-interventions (30.3%), blinding of outcome assessors (53.8%), specific measures to avoid ascertainment bias (28.6%), and intention-to-treat analysis (60.2%). CONCLUSIONS: Although we showed no difference in effect between the intervention and control groups, our results highlight the gap in knowledge and urgency for education on important aspects of trial conduct.
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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 | 0.128 | 0.231 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".