Creation of an online inventory for choosing critical appraisal tools
Bibliographic record
Abstract
Critical appraisal of evidence is performed to assess its validity, trustworthiness and usefulness in evidence-based practice. There currently exists a large number and variety of critical appraisal tools (also named risk of bias tools and quality assessment instruments), which makes it challenging to identify and choose an appropriate tool to use. We sought to develop an online inventory to inform librarians, practitioners, graduate students, and researchers about critical appraisal tools. This online inventory was developed from a literature review on critical appraisal tools and is kept up to date using a crowdsourcing collaborative web tool (eSRAP-DIY). To date, 40 tools have been added to the inventory (www.catevaluation.ca), and grouped according to five general categories: (a) quantitative studies, (b) qualitative studies, (c) mixed methods studies, (d) systematic reviews and (e) others. For each tool, a summary is provided with the following information: tool name, study designs, number of items, rating scale, validity, reliability, other information (such as existing websites or previous versions), and main references. Further studies are needed to test and improve the usability of the online inventory, and to find solutions to reduce to monitoring and update workload.
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 | 0.308 | 0.531 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.040 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".