The revised JBI critical appraisal tool for the assessment of risk of bias for quasi-experimental studies
Bibliographic record
Abstract
Systematic reviews of effectiveness offer a rigorous synthesis of the best evidence available regarding the effects of interventions or treatments. Randomized controlled trials are considered the optimal study design for evaluating the effectiveness of interventions and are the ideal study design for inclusion in a systematic review of effectiveness. In the absence of randomized controlled trials, quasi-experimental studies may be relied on to provide information on treatment or intervention effectiveness. However, such studies are subject to unique considerations regarding their internal validity and, consequently, the assessment of the risk of bias of these studies needs to consider these features of design and conduct. The JBI Effectiveness Methodology Group has recently commenced updating the suite of JBI critical appraisal tools for quantitative study designs to align with the latest advancements in risk of bias assessment. This paper presents the revised critical appraisal tool for risk of bias assessment of quasi-experimental studies; offers practical guidance for its use; provides examples for interpreting the results of risk of bias assessment; and discusses major changes from the previous version, along with the justifications for those changes.
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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 | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.553 | 0.817 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.042 | 0.038 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".