PROTOCOL: Systematic review of methods to reduce risk of bias in knowledge translation interventional studies in health‐related issues
Bibliographic record
Abstract
Background: Review studies have reported on the low quality of study methodologies and poor reporting of knowledge translation (KT) interventional studies. This flaw cause the result of such studies to become misleading. Objectives: The present review is designed to evaluate the effect of methodological factors on the results of interventional studies that aimed to evaluate KT strategies at the policy level. Search Methods: Bibliographic databases and grey literature databases will be searched. The retrieved studies will be recorded in Covidence. After screening titles and abstracts, the full texts of selected studies will be assessed against the inclusion criteria. Disagreements will be resolved through discussion or by consultation with a third author. Selection Criteria: Primary studies are studies that aimed to estimate the efficacy of KT strategies to improve evidence-informed policymaking. Study participants include policymakers and the intervention is a KT strategy. The main outcome is the desired changes in policy-makers towards evidence-informed decision-making. Data Collection and Analysis: The main effect sizes will be expressed as standard mean difference and its variance for the main efficacy outcome of KT strategies in primary studies. Forest plot meta-analysis will be used to synthesize the effect of each group of KT strategies. The contribution of ROB to the efficacy of KT interventions will be assessed via Meta-epidemiology analysis. The overall estimate will be calculated using inverse-variance random-effects meta-analysis with a 95% confidence interval for the estimate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.221 | 0.351 |
| Meta-epidemiology (narrow) | 0.010 | 0.007 |
| Meta-epidemiology (broad) | 0.025 | 0.021 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.070 | 0.016 |
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".