A collaboration for the promotion of evidence synthesis: A Canadian-African Partnership
Bibliographic record
Abstract
In 2018, the Queen’s Collaboration for Health Care Quality: A Joanna Briggs Institute Centre of Excellence (QcHcQ) spearheaded an incentive to increase collaboration and international partnerships. As part of this initiative, six library scientists from the partner institutions of the Consortium for Advanced Research Training in Africa (CARTA) were invited to Queen’s University in Kingston, Ontario to undertake training. The objective was to provide these library scientists with a comprehensive systematic review-training workshop using the Joanna Briggs Institute methodology for evidence synthesis. The intense six-day training workshop covered evidence synthesis of quantitative evidence and qualitative evidence as well as multiple methodologies for the synthesis of different levels of evidence. As a continuation of the collaboration a joint systematic review was embarked on titled: The role of library scientists in fostering evidence based health care.
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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 | 0.490 | 0.519 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".