Rapid Reviews Methods Series: Involving patient and public partners, healthcare providers and policymakers as knowledge users
Bibliographic record
Abstract
Rapid reviews (RRs) are a helpful evidence synthesis tool to support urgent and emergent decision-making in healthcare. RRs involve abbreviating systematic review methods and are conducted in a condensed timeline to meet the decision-making needs of organisations or groups that commission them. Knowledge users (KUs) are those individuals, typically patient and public partners, healthcare providers, and policy-makers, who are likely to use evidence from research, including RRs, to make informed decisions about health policies, programmes or practices. However, research suggests that KU involvement in RRs is often limited or overlooked, and few RRs include patients as KUs. Existing RR methods guidance advocates involving KUs but lacks detailed steps on how and when to do so. This paper discusses the importance of involving KUs in RRs, including patient and public involvement to ensure RRs are fit for purpose and relevant for decision-making. Opportunities to involve KUs in planning, conduct and knowledge translation of RRs are outlined. Further, this paper describes various modes of engaging KUs during the review lifecycle; key considerations researchers should be mindful of when involving distinct KU groups; and an exemplar case study demonstrating substantive involvement of patient partners and the public in developing RRs. Although involving KUs requires time, resources and expertise, researchers should strive to balance 'rapid' with meaningful KU involvement in RRs. This paper is the first in a series led by the Cochrane Rapid Reviews Methods Group to further guide general RR methods.
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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: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| 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 | 0.463 | 0.669 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.051 | 0.037 |
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".