Bibliographic record
Abstract
Introduction: With knowledge synthesis (KS) projects becoming increasingly popular research projects, it is not uncommon that many are poorly conceived.Protocols are an integral planning component to the KS process, so why are so many below par and not well-thought out?As information specialists collaborating on almost 40 KS projects a year, we frequently encounter research teams that believe the review starts with the search and simultaneously our roles have expanded to providing extensive feedback on protocols.This poster outlines the outcomes and uptake of an improved, user-focused KS protocol template following its pilot-testing process, workshop demonstration, and its launch on a web-based platform.Description: A rigorous revision of a hospital library team's KS protocol template was completed in January 2023.To ensure the success and uptake of the new protocol template, a pilot-testing process among key researchers in the organization will be undertaken and a workshop demonstration and explanation will be conducted.It is anticipated that the revised KS protocol will create a more sustainable KS workflows for the library team.Outcomes: Feedback from clients and statistics on the uptake of the new KS protocol template will be shared.The presenters will share how the improved protocol template has impacted time spent on revising protocols.Attendees will learn how to improve their own protocol templates to ensure more sustainable KS collaborations.Discussion: This poster will discuss how to evaluate and successfully implement changes to a protocol template so that KS collaborations are more efficient and produce higher-quality work.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.338 | 0.169 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".