Textual evidence systematic reviews series paper 3: critical appraisal of evidence from narrative, opinion, and policy
Bibliographic record
Abstract
JBI has long held the view that an inclusive approach to the conceptualization of what counts as evidence is important to the evidence-based movement. JBI's approach for appraising textual evidence had encompassed all forms of text (narrative, opinion, and policy), with one general tool used to guide critical appraisal. The proliferation of textual evidence and increase in textual evidence reviews demonstrate the need to reconceptualize JBI's methodological approach to critically appraising textual evidence. The objective of this paper is to outline the updated methodological approach to systematic reviews of textual evidence, especially in relation to the development of 3 separate critical appraisal tools for narrative, expert opinion, and policy text. Using an adapted Delphi approach, the JBI Textual Evidence Methodology Group convened over several rounds of meetings and discussions with international experts to reach consensus on the reconceptualization of critical appraisal tools for textual evidence sources. Strategies to effectively interrogate the legitimacy and authenticity of sources were found to be dependent upon the type of textual evidence under review. Therefore, 3 separate critical appraisal tools for narrative, expert opinion, and policy text were developed. This paper provides an overview of the development of 3 separate critical appraisal tools, highlighting the complex nature of textual evidence data sources.
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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 | 0.295 | 0.495 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.032 | 0.022 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".