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Knowledge user involvement is still uncommon in published rapid reviews—a meta-research cross-sectional study

Published online by Cambridge University Press:  10 July 2025

Barbara Nussbaumer-Streit Open the ORCID record for Barbara Nussbaumer-Streit [Opens in a new window] ,
Dominic Ledinger ,
Christina Kien ,
Irma Klerings ,
Emma Persad ,
Andrea Chapman ,
Claus Nowak ,
Arianna Gadinger ,
Lisa Affengruber  and
Maureen Smith
...Show all authors
Barbara Nussbaumer-Streit*
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Dominic Ledinger
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Christina Kien
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Irma Klerings
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Emma Persad
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Andrea Chapman
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Claus Nowak
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Arianna Gadinger
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Lisa Affengruber
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
Maureen Smith
Affiliation:
Cochrane Consumer Network Executive, London, UK
Gerald Gartlehner
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria RTI International, Research Triangle Park, NC, USA
Ursula Griebler
Affiliation:
Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Cochrane Austria, Krems, Austria
*
Corresponding author: Barbara Nussbaumer-Streit; Email Barbara.nussbaumer-streit@donau-uni.ac.at
Rights & Permissions [Opens in a new window]

Abstract

Background

Involving knowledge users (KUs) such as patients, clinicians, or health policymakers is particularly relevant when conducting rapid reviews (RRs), as they should be tailored to decision-makers’ needs. However, little is known about how common KU involvement currently is in RRs.

Objectives

We wanted to assess the proportion of KU involvement reported in recently published RRs (2021 onwards), which groups of KUs were involved in each phase of the RR process, to what extent, and which factors were associated with KU involvement in RRs.

Methods

We conducted a meta-research cross-sectional study. A systematic literature search in Ovid MEDLINE and Epistemonikos in January 2024 identified 2,493 unique records. We dually screened the identified records (partly with assistance from an artificial intelligence (AI)-based application) until we reached the a priori calculated sample size of 104 RRs. We dually extracted data and analyzed it descriptively.

Results

The proportion of RRs that reported KU involvement was 19% (95% confidence interval [CI]: 12%–28%). Most often, KUs were involved during the initial preparation of the RR, the systematic searches, and the interpretation and dissemination of results. Researchers/content experts and public/patient partners were the KU groups most often involved. KU involvement was more common in RRs focusing on patient involvement/shared decision-making, having a published protocol, and being commissioned.

Conclusions

Reporting KU involvement in published RRs is uncommon and often vague. Future research should explore barriers and facilitators for KU involvement and its reporting in RRs. Guidance regarding reporting on KU involvement in RRs is needed.

Keywords

interst holder involvementstakeholder involvementevidence synthesisrapid reviews

Information

Type
Research Article
Information
Research Synthesis Methods , Volume 16 , Issue 6 , November 2025 , pp. 876 - 899
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Open Practices
Open data
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Society for Research Synthesis Methodology

Highlights

What is already known?

  • • Although knowledge user (KU) involvement can increase the relevance of rapid reviews (RRs) for decision-making, previous studies have shown that only 26%–43% of RRs involved KUs. Following the increased production of RRs in recent years, alongside new RR methods guidance emphasizing the importance of KU involvement, we wanted to know where we stand with KU involvement in RRs.

What is new?

  • • KU involvement in RRs is still very uncommon and its reporting is limited. If an RR involved KUs, these were most often researchers/content experts or public/patient partners. They were most often involved at the beginning and the end of the RR process.

Potential impact for RSM readers

  • • Our study highlights that KU involvement and its reporting in rapid evidence synthesis can still be greatly improved. Developing and evaluating methods for KU involvement that work within time and resource constraints would be essential to improve KU involvement in RRs. In addition, guidelines for RRs should emphasize the importance of reporting KU involvement.

1 Background

Rapid reviews (RRs) are a type of evidence synthesis that use modified systematic review (SR) methods to accelerate the review process and complete the review in a timely and resource-efficient manner.Reference Hamel, Michaud and Thuku1 RRs are appealing to healthcare decision-makers because they can synthesize evidence quickly, particularly in urgent situations. Involving knowledge users (KUs) when conducting evidence syntheses is recommended to increase the review’s relevance.Reference Garritty, Tricco, Smith, Pollock, Kamel and King2, Reference Hartling, Guise and Hempel3 We define KUs as individuals who are likely to use the synthesized evidence to make informed decisions about clinical or health policy interventions or who might be affected by these decisions.4 By applying this definition, KUs can include individuals such as clinicians or other healthcare providers, health policymakers, patients, members of patient organizations, caregivers, or the general public.Reference Garritty, Tricco, Smith, Pollock, Kamel and King2

KU involvement is especially relevant in the context of RRs, as certain KUs often commission or request RRs. To make the RR as valuable and relevant as possible, close collaboration with the end user, e.g., the commissioner, is recommended by RR guidance.Reference Dobbins5, Reference Tricco, Langlois and Straus6 However, involving KUs is not yet the norm. Previous studies have shown KU involvement to range from 26% to 43% in RRs.Reference Feldmann, Puhan and Mütsch7- Reference Griebler, Dobrescu and Ledinger9 In particular, the involvement of patients, who are primarily affected by health decisions but are usually not the commissioners of RRs, is often neglected. Notably, only 6% of the 103 RRs analyzed by Garritty et al. involved patients in the RR process.Reference Garritty, Hamel and Hersi8

There are various methods and levels of engagement for involving KUs in the RR process. According to the Authors and Consumers Together Impacting on eVidencE (ACTIVE) framework,Reference Pollock, Campbell and Struthers10 developed for SRs, KU involvement can be seen as a continuum from informing KUs about a review to involving them in one or more phases of the RR process (e.g., formulating the question and choosing the outcomes), or co-producing the complete review with them.Reference Pollock, Campbell and Struthers10

While health policymakers and health organizations had already used RRs before the COVID-19 pandemic,Reference Hartling, Guise and Hempel3, Reference Mijumbi-Deve, Rosenbaum, Oxman, Lavis and Sewankambo11- Reference Thigpen, Puddy, Singer and Hall15 the urgent need for evidence syntheses during the pandemic has worked as a catalyst for RRs, and the number of published RRs has increased tremendously. A PubMed search for articles with the term “rapid review” in the title retrieved approximately 250 records published until 2019, with more than 1,100 records published between 2020 and September 2024. In parallel to the increased demand for RRs, awareness of the importance of KU involvement has increased. While previous RR methods guidance has already recommended involving stakeholders in the RR process, such as commissioners, policymakers, and health system managers in the RR process,Reference Dobbins5, Reference Tricco, Langlois and Straus6 the RR methods guidance published online in October 2020 explicitly recommended involving a broad range of KUs, including patients and health professionals, throughout the whole RR process.Reference Garritty, Gartlehner and Nussbaumer-Streit16

1.1 Aim and research questions

We wanted to assess the proportion of KU involvement reported in recently published RRs (2021 onward). We also wanted to gain insight into which groups of KUs were involved in the specific phases of the RR process and the extent of their involvement. In addition, we wanted to explore whether the proportion of KU involvement was associated with certain factors. Specifically, we wanted to answer the following research questions:

  1. 1. What is the proportion of KU involvement reported in RRs published since 2021?

  2. 2. In what phases of RRs are KUs involved?

  3. 3. To what level are KUs involved in RRs?

  4. 4. Which KU groups are involved in RRs?

  5. 5. To what extent is KU involvement associated with other factors (review type, year of publication, review topic, main rationale for choosing a rapid approach, RR protocol in place, citing an RR methods guidance, funding source, region of funding, and timeline of the RR)?

2 Methods

2.1 Study design

We conducted a meta-research cross-sectional study to assess the proportion of KU involvement reported in RRs published since 2021. We chose this cut-off as new methods guidance for RRs was published online in October 2020, specifically recommending broad KU involvement,Reference Garritty, Gartlehner and Nussbaumer-Streit16 and due to the COVID-19 pandemic, the number of published RRs increased markedly. We published our protocol at the Open Science Framework (https://osf.io/gkm58/). When applicable, we adhered to the Strengthening the Reporting of Observational studies in Epidemiology (STROBE)Reference von Elm, Altman, Egger, Pocock, Gøtzsche and Vandenbroucke17 reporting guideline throughout the manuscript.

2.2 Patient and public involvement statement

We involved a patient partner (MS) as a member of the project team, who provided input on the research questions, the data extraction form, the analyses and interpretation of data, and contributed to writing the manuscript.

2.3 Sample size calculation

We used a precision-based approach for sample size determination, where the aim was to enclose the exact two-sided 95% confidence limits for a binomial proportion (Clopper–Pearson method) within an interval at most 0.20 wide, regardless of the actual value of the proportion that we will observe in the sample.Reference Brown, Cai and DasGupta18– Reference Newcombe20 Therefore, we required a sample size of 104 RRs.

2.4 Identifying eligible RR

We conducted a systematic literature search in Ovid MEDLINE and Epistemonikos.org on January 09, 2024, to identify published RRs. We chose these two databases as we expected them to include most published RRs of studies in humans and health science. The search strategies are presented in detail in Appendix 1 of the Supplementary Material. Search terms were based on terminology identified by Hamel et al.Reference Hamel, Michaud and Thuku1 To be eligible during the study selection phase, RRs had to use the term “rapid” in the title or abstract, had to be published in a peer-reviewed journal since 2021, and needed to be focused on a health-related topic. We were interested in any type of rapid evidence syntheses (e.g., RR of interventions, rapid qualitative evidence synthesis, rapid scoping review, etc.) published in any language and from any geographic region. We excluded publications that did not define themselves as rapid evidence syntheses, such as systematic reviews, and we excluded methods studies about RRs and protocols of RRs. We focused on RRs of studies in humans, as these are mostly used for decision-making in health care; therefore, we excluded RRs of laboratory or animal studies. When the RR was only part of a larger study and RR methods were not reported in detail, we also excluded the publication.

2.5 Study selection

We uploaded all identified records to DistillerSR.21 The use of this software was a deviation from the protocol, as we had originally planned to use Covidence and conduct all abstract/title screening dually and independently. Instead, we decided to use “DistillerSR Artificial Intelligence SYstem” (DAISY) integrated into DistillerSR to support study selection. We conducted a pilot exercise to calibrate the title/abstract screening among the whole team (B.N.S., U.G., C.K., D.L., L.A., E.P., A.G., and A.C.), using a sample of 50 records to screen.

At the title/abstract level, we screened 33% of the identified records dually by independent screeners. Because we had very high agreement in our inclusion/exclusion decisions (κ of 0.93), we switched to single human screening and AI confirmation. This means, the remaining 67% of records were screened by one person and the AI screening tool DAISY (part of the systematic review software DistillerSR). Disagreements between the human and the AI screener were resolved by the principal investigator (B.N.S.).

We exported the list of all potentially relevant records to an Excel sheet and assigned computer-generated random numbers to each record. We then screened the full-texts dually and independently in order of their randomly assigned number until we reached 104 eligible RRs (see Figure 1).

Figure 1

PRISMA 2020 Flow diagram.

2.6 Data extraction

We created a data extraction form in Excel. We pilot-tested the data extraction form on the same four RRs with all team members involved in extracting data. We discussed and clarified any uncertainties that arose during this phase and revised the form. After incorporating feedback from the piloting exercise, we conducted dual data extraction of the remaining 100 RRs (B.N.S., C.K., U.G., E.P., D.L., A.G., and A.C.). We extracted information on RR characteristics (first author’s name, year of publication, journal, type of RR, focus of the RR, rationale for choosing a rapid approach, RR protocol in place, citing an RR methods guidance, commissioner/funding, timeline of the RR) and if KU involvement was reported. If there were hints of KU involvement, but it was somewhat unclear (e.g., mentioning names in the acknowledgment section without specifying their role in the RR), we contacted the corresponding author of the RR and asked for clarification. When an RR reported KU involvement, we extracted to what extent different groups of KUs were involved for each phase of the RR and if any information on potential conflicts of interest of KUs was reported. Based on feedback from the patient partner (MS), we also extracted whether authors used a framework to report KU involvement, whether they indicated if KU involvement was mandatory (e.g., if a funder made it mandatory), whether KUs were compensated for their contributions, whether they were mentioned as co-authors, or whether the authors explicitly reflected on KU involvement.

2.7 Data analysis

Our primary outcome of interest was the proportion of KU involvement reported in RRs published since January 2021. We applied a broad definition of KU involvement. We considered KU involvement to be any involvement of at least one person who can be regarded as KU during at least one step of the review process. To define a KU, we considered any person who potentially uses synthesized evidence for decision-making in health care, comprising individuals such as clinicians, healthcare providers, health policymakers, commissioners, patients, members of patient organizations, caregivers, or the general public. As reporting of KU involvement was sometimes unclear, we focused on the proportion of reported KU involvement, which may differ from the actual KU involvement if not reported in the peer-reviewed paper. If an RR only reported names in the acknowledgment sections without clarifying their role in the RR process, we did not consider this as KU involvement. To calculate the proportion of KU involvement in RRs, we took the number of RRs with explicitly reported KU involvement of any kind and divided it by the total number of all 104 analyzed RRs.

We used a descriptive approach to summarize the KU groups that were involved and the intensity of KU involvement (e.g., one-time or regular involvement) in each RR phase (preparation [e.g., formulating the research question and deciding on outcomes], searching, study selection, data extraction, risk of bias assessment, synthesis, interpreting findings, communication of results).

To explore whether other factors were associated with KU involvement, we also chose a descriptive approach. We looked at the following factors: review type, year of publication, review topic, main rationale for choosing a rapid approach, RR protocol in place, citing an RR methods guidance, funding source, region of funding, and timeline of the RR. We calculated the proportion of RRs with KU involvement within these subgroups and compared it with the proportion of the general sample. We did not compute statistical tests as the nature of the analysis was exploratory and due to the small amount of data. All analyses were performed using Microsoft Excel.

Since the proportion of RRs with unclear KU involvement was considerable (n = 11), we contacted their authors and asked for clarification. Five of eleven authors replied to us. We used the provided information in a sensitivity analysis presented at the end of the results section. However, the main analyses for all research questions focused on reported KU involvement. We chose not to mix KU involvement reported in the publication and KU solely described via email, as we received responses from only five out of eleven contacted authors, and we did not contact the 70 authors of RRs without any mention of KU involvement.

3 Results

Out of 2,493 records identified by our literature search, we included 104 randomly selected RRsReference Wang, Wang, Wang and Luan22- Reference Brooks, Greenberg, Wessely and Rubin125 that fulfilled our eligibility criteria (see Figure 1; PRISMA 2020Reference Page, McKenzie and Bossuyt126).

3.1 Characteristics of included RRs

Our sample consisted of a diverse range of RR types, with the largest groups being RRs of interventions (31%; 32/104) and rapid scoping reviews (30%; 31/104). Most of the included RRs focused on research questions regarding noncommunicable diseases (33%; 35/104), COVID-19 (31%; 32/104), or healthcare delivery and systems (29%; 30/104). Over half of the RRs (53%; 55/104) did not report a rationale for conducting the evidence synthesis rapidly. Those giving a rationale mentioned “time constraints and the need to make an urgent decision” as the main reason for choosing a rapid approach (39%; 41/104). Half of the RRs had reported having a protocol in place, and 84% (87/104) cited RR methods guidance. Only 12% (13/104) of the RRs were commissioned; others reported general research funding (55%; 57/104). Commissioners or funding bodies were mainly from Europe (40%; 28/70), North America (29%; 20/70), and Australia/New Zealand (14%; 10/70). The median time from conducting the last search to submitting the RR for publication was 6 months, ranging from 0 to 31 months in 67 RRs reporting this information. The median time from the last search to the publication of the RR was 11.5 months, ranging from 1–49 months in 88 RRs reporting this data. For more details on the characteristics of the included RRs, see Table 1.

Table 1

Characteristics of included rapid reviews

Abbreviations: COVID-19, coronavirus disease 2019; RR, rapid review; n, number of reviews.

3.2 Reported knowledge of user involvement in RRs

3.2.1 Proportion of KU involvement

Of the 104 included RRs, 20 reported KU involvement. This results in a proportion of reported KU involvement of 19% (95% confidence interval [CI] 0.12–0.28). In additional 11% (11/104) of RRs, KU involvement was unclear. There were hints, such as names mentioned in the acknowledgments section, but no explicit description of KU involvement throughout the publication. In the remaining 70% (73/104) of RRs, KU involvement was clearly not mentioned. We contacted the authors of the 11 RRs with unclear KU involvement via email. Five clarified that they did involve KU, the other six did not respond.

For the following analysis, we focused on the 20 RRs that reported KU involvement in the publication.Reference Stich, Lakrouf and Moreau25, Reference Russell, Aouad and Le30, Reference Palese, Chiappinotto, Bayram, Sermeus, Suhonen and Papastavrou31, Reference Corp, Bray and Chew-Graham40, Reference Ryan, Silke and Parkhill41, Reference Karlsson, Kragh-Sorensen and Borgesen48, Reference Carroll, Kobayashi, Cervantes, Freeman, Saini and Tracey49, Reference Gkiouleka, Aquino and Ojo-Aromokudu57, Reference Bryant, Spielman, Le, Marks, Touyz and Maguire59, Reference Wingert, Pillay and Gates75, Reference Upshaw, Brown, Smith, Perri, Ziegler and Pinto83, Reference Stirling, Willcox, Ong and Forsyth85, Reference Kadowaki, Yamamoto and Taneda89, Reference O'Reilly, Tibbs, Booth, Doyle, McKeague and Moore97, Reference Ghidei, Montesanti, Tomkow, Silverstone, Wells and Campbell99, Reference Butow, Shaw and Bartley103, Reference Toft, Rodkjaer and Andersen109, Reference Clyne, Jordan and Ahern115, Reference Barnett, Steare and Dedat119, Reference Gentry and Paterson120 We considered the five additional RRs that reported KU involvement upon requestReference Hibbert, Simmons, Mandal, Sabin and Desai36, Reference Crowe, Barker and Roberts64, Reference Exley, Gupta and Schellenberg104, Reference Okoli, Rabbani and Lam113, Reference Geneen, Kimber, Doree, Stanworth and Shah114 in a sensitivity analysis at the end of the results section.

Table 2 gives more information on the characteristics of the RRs with reported KU involvement.

Table 2

Details of rapid reviews with reported knowledge user involvement

Abbreviations: ACTIVE, Authors and Consumers Together Impacting on eVidencE; CoI, conflict of interest; GRIPP, Guidance for Reporting Involvement of Patients and the Public; KU, knowledge user; NR, not reported; RR, rapid review; QES, qualitative evidence synthesis.

Explanation: other type of review = e.g., economic review, mixed methods review

3.2.2 Knowledge user involvement during RR phases

Seventy percent (14/20) of the RRs with KU involvement engaged KUs in the preparation of the RR to ensure the topic and research questions are relevant to the KU’s needs. Half of the RRs (10/20) involved KUs during searching to help with the development of the search strategy, find the best search terms, and ensure that all important sources were searched. Forty percent (8/20) of the RRs involved KUs during the interpretation of findings and communication of results (writing the report, disseminating the results). Only a small proportion (5%–15%) involved KUs during study selection, data extraction, risk of bias assessment, and synthesis (see Table 3).

Table 3

Involvement of knowledge users during the rapid review process

3.2.3 Level of KU involvement

Reporting on the level of KU involvement was rather vague. Often, authors used general terms such as working closely or being consulted without further specifications of how frequently and in what way (e.g., via survey, workshops, meetings, written feedback) this was done. To determine whether involvement was continuous or only once during the project, we assessed whether KUs were involved in one, multiple, or all steps of the RR process. Forty percent (8/20) reported KU involvement only during one step of the review process, 55% (11/20) involved them in multiple steps of the review process, and 5% (1/20) involved the KUs throughout the whole RR process (see Table 3).

In 20% of RRs (4/20), the involved KUs were clearly part of the review author team. No publication reported financial compensation of the involved KUs; the involved KU was the commissioner in 10% of the RRs (2/20), so one can assume the KU could contribute during working hours. One RR provided a conflict-of-interest statement for KUs (see Table 2).

In 20% of RRs (4/20), authors explicitly reflected on KU involvement: One RR (5%) provided a lived experience commentary, one RR (5%) focused per se on the topic of patient involvement, and two (10%) described the value of involving KUs in the process. These two were the only ones that used a KU involvement tool (ACTIVE FrameworkReference Pollock, Campbell and Struthers10 or GRIPP IIReference Staniszewska, Brett and Simera127) (see Table 2).

3.2.4 KU groups involved

In 80% (16/20) of the RRs, only one KU group was involved. In the remaining 20% (4/20) RRs, multiple KU groups were involved.

Overall, 55% (11/20) involved content experts/researchers from across the relevant research fields; 35% (7/20) involved patient and/or public partners ranging from consumers and patients with lived experience to carers and local community organizations. In 25% (5/20) of the RRs, healthcare professionals were involved. Two RRs involved policymakers and one the commissioner/funder. When involving multiple KU groups, three RRs engaged “patient and/or public partners” along with “healthcare professionals,” “researchers/content experts,” or “healthcare professionals, policymakers, and researchers/content experts.” A fourth RR involved “healthcare professionals” along with “policymakers” (see Table 2).

3.2.5 Factors associated with KU involvement in RRs

To determine if those RRs with KU involvement differed in any factors of interest, we calculated the proportion of KU involvement per subgroup (review type, year of publication, review topic, rationale for rapid approach, protocol, RR methods guidance, funding source, and funding country). As the general proportion of RRs with reported KU involvement was 19% in the sample, we considered this as the expected proportion (black line in Figure 2). As the number of RRs in the subgroups analyzed was often small, the results need to be interpreted with caution. Factors that might be associated with a higher proportion of KU involvement were review topics on patient involvement/shared decision-making and RRs that were explicitly commissioned. A lower proportion of KU involvement was seen in RRs that did not cite an RR methods guidance or reported no funding. For further details, see Figure 2.

Figure 2

Reported knowledge user involvement per subgroup.

When assessing the time from the last literature search to the submission of the RR, the median time for RRs with KU involvement was 7 months (range 1–26, n = 15), compared to 6 months (range 0–31, n = 67) in the general sample. When analyzing the time from the search to the final publication of the RR, the median time for RRs that involved KUs was 15 months (range 3–28; n = 19) compared to the general sample (median 11.5 months; range 1–49; n = 88).

3.2.6 Sensitivity analysis

Authors of five RRsReference Hibbert, Simmons, Mandal, Sabin and Desai36, Reference Crowe, Barker and Roberts64, Reference Exley, Gupta and Schellenberg104, Reference Okoli, Rabbani and Lam113, Reference Geneen, Kimber, Doree, Stanworth and Shah114 provided information on KU involvement upon request via email. We did not include this information in the main analysis as outlined in the methods section. To explore the impact of this decision on our research questions, we conducted sensitivity analyses adding these five RRs and calling it “clarified KU involvement.”

Proportion of KU involvement. When adding these five RRs, the proportion of “clarified KU involvement” was 24% (95% CI 0.16–0.33).

Characteristics of RRs. Of these five RRs, two were RRs of interventions, two were rapid scoping reviews, and one was categorized as “other RR type.” One focused on infectious disease, two on noncommunicable diseases, and two on healthcare delivery. Four of them cited RR methods guidance, three had a published protocol, and all five had funding. The time from the last search to publication ranged from 6 to 17 months.

Phases and level of KU involvement. When adding these five RRs, the proportion of KU involvement during the RR phases did not change markedly compared to the main analysis based on the 20 RRs. KU involvement was 68% during preparation (17/25), 56% during searching (14/25), 20% during study selection (5/25), 16% during data extraction (4/25), 8% during risk of bias assessment (2/25), 16% during synthesis (4/25), 48% during interpretation (12/25), and 48% (12/25) during communication.

Interestingly, four of these five RRs had KU listed as co-authors. One RR involved KU in all phases of the review, three in multiple phases, and one only in one phase.

Groups of KU involved. When adding these five RRs to the main analysis, the proportion of RRs involving content experts/researchers was still the largest at 48% (12/25), followed by patient and/or public partners at 40% (10/25), and healthcare professionals at 24% (6/25). None of these five RRs involved a funder or a policymaker.

Factors associated with KU involvement. When adding these five RRs to the exploratory analysis of factors associated with KU involvement, we did not observe major changes to our main analysis (see Figure 3).

Figure 3

Knowledge user involvement per subgroup (main analysis vs. sensitivity analysis).

4 Discussion

Out of 104 RRs, only 19% (n = 20) reported KU involvement. This is lower than in previous studies, where KU involvement ranged from 26% to 43%.Reference Feldmann, Puhan and Mütsch7- Reference Griebler, Dobrescu and Ledinger9 Additionally, 11% of the 104 assessed RRs had “unclear” KU involvement, with acknowledgments listing individuals but not clarifying their roles. When contacted, five authors replied, and all confirmed KU involvement that had not been reported in their RRs. This could be an indicator of potential underreporting of KU involvement and lack of awareness that KU involvement should be reported. In four of the five RRs, the KUs were part of the author team. Eventually, research teams considered the acknowledgment of RR contribution by co-authorship to be sufficient and that it did not need to be reported separately. In RRs with KU involvement, KUs typically contributed to early phases of the RR process (topic refinement, search), aligning with previous findings,Reference Feldmann, Puhan and Mütsch7 or at the end, with interpreting and disseminating the findings. Although KU involvement is recommended throughout the whole RR process, it might be appropriate not to include some KU groups in all phases. For example, commissioners could have inherent interests or even conflicts of interest regarding specific findings and therefore should not be involved in the analysis part.

Garrity et al. found that while patient involvement was similarly reported in published and non-published RRs, commissioner involvement was underreported in published RRs.Reference Garritty, Hamel and Hersi8 This may reflect why some RRs commissioned for urgent decision-making are not published in journals, or the strict word limit that most journals demand precludes authors from reporting KU involvement clearly. In our sample, one-third of the RRs were on COVID-19 and conducted for urgent decision-making needs, which may have precluded author teams from KU involvement. Time from the last search to submission of the publication was 1 month longer (median) in RRs with KU than in the general sample. While this can be perceived as a marginal difference in the overall review timeline, it can be significant in the context of urgent decision-making needs.

In our sample, content experts/other researchers were most often reported as KUs. It is possible that not all review teams were aware that involving other experts in the field could be considered as KU involvement. Patients and public partners were the second most often involved KU groups (n = 7 RRs). Similar results have been shown by Garritty et al.Reference Garritty, Hamel and Hersi8 in a sample of published and non-published RRs from 2016. However, newer RR methods guidanceReference Garritty, Hamel and Trivella128 explicitly recommends their involvement to ensure the RRs are relevant for those affected by health-care decisions.

Exploratory analysis indicated factors associated with higher KU involvement, namely a focus on patient involvement/shared decision-making, explicit commissioning, or having a published protocol. In contrast, RRs that did not cite any RR methods guidance or that reported no funding had less often reported KU involvement. These factors may serve as facilitators or barriers, though the small subgroup sizes caution against overinterpretation. Ongoing research, such as the Multi Stakeholder Engagement (MUSE) consortium’s workReference Tugwell, Welch and Magwood129 will shed further light on KU involvement by focusing exclusively on evidence syntheses with KU involvement, allowing for a larger sample size.

An interesting finding of our study was the diversity of RR types. Although originally focused on assessing intervention benefits and harms, many RRs are now being conducted as rapid scoping or qualitative evidence syntheses, reflecting a broader application of the RR process. This aligns with the evolving taxonomy of evidence synthesis, where “rapid” is considered a production mode rather than a specific review type.130

Our study, while comprehensive in its review of KU reporting across various RR types, has limitations. First, we focused on reported KU involvement, which may not capture actual involvement, as our findings suggest underreporting. This highlights the need for specific reporting guidelines for KU involvement in RRs. Second, we only analyzed published RRs, which may not fully reflect KU involvement in non-published RRs conducted for decision-making purposes. However, we wanted to assess the current situation in published RRs, as this is the product that is accessible to the research and decision-making community globally. Third, we used AI to assist with screening rather than dually screening all records. However, given the high agreement within the team and our random sampling approach, this likely had minimal impact on the results. Finally, we included only reviews explicitly labeled “rapid,” even though we searched using terms like “swift” or “targeted,” relying on the authors’ definitions of RR as we could not verify methods from abstracts alone. However, we assessed the methods of the included RRs and all applied at least one methods abbreviation compared to the traditional SR approach, so we are confident that our sample truly represented RRs.

Despite these limitations, our study provides valuable insights into the current state of KU involvement in RRs and highlights future research needs. Reporting remains scarce, likely due to the time and resource constraints inherent in RR processes and the strict word limits demanded by many journals. Many researchers may also not be aware that they should report KU involvement. Future research should explore barriers and facilitators to KU involvement in RRs, with a particular focus on the challenges posed by the rapid approach. A systematic review assessing challenges of public and patient involvement in evidence syntheses found influencing factors on both sides: the KUs and the researcher teams. One hindering factor on both sides was a lack of time.Reference Agyei-Manu, Atkins, Lee, Rostron, Dozier, Smith and McQuillan131 The MUSE consortium also plans to conduct a qualitative evidence synthesis on barriers and facilitators of KU involvement in systematic reviews.Reference Tugwell, Welch and Magwood129 We would like to emphasize that there should be a specific attention to the assessment of KU involvement in the RR context, as factors like limited time and resources might differ in comparison to the traditional systematic review context. Developing and evaluating resource-efficient methods for KU involvement that work within time and resource constraints would also be essential to improve KU involvement in RRs. Future research should learn from best practice examples such as RR-producing entities like the COVID-END (COVID 19 Evidence Network to support Decision-Making) project132 and the SPOR (Strategy for Patient-Oriented Research) Evidence Alliance,133 which regularly include the public in the RR process. Additionally, to explore the actual extent of the underreporting of KU involvement or the perceptions and inherent definitions of authors, it would be interesting to assess KU involvement in a random sample via questioning the RR authors and comparing this information with the KU involvement reported in publications. Also, the forthcoming PRISMA-RR reporting guideline (OSF LinkReference Stevens, Tricco and Straus134) should include a specific item on KU involvement, detailing who was involved, at what phases of the review, and how. The broad range of RR types also indicates a need for future methods studies assessing the impact of RR methods on different types of rapid evidence synthesis, such as rapid scoping reviews or rapid qualitative evidence syntheses.

Acknowledgments

We would like to thank Sandra Hummel for her administrative support of the project.

Author contributions

Conceptualization: B.N.S., U.G., C.K., D.L., I.K., E.P., A.C., A.G., L.A., C.P.N., G.G.; Data curation: B.N.S., U.G., C.K., D.L., E.P., A.C., A.G., M.S.; Formal analysis: B.N.S., U.G., C.K., D.L., M.S., G.G.; Funding acquisition: B.N.S., G.G.; Investigation: B.N.S., U.G., C.K., D.L., E.P., A.C., A.G., L.A.; Methodology: B.N.S., C.P.N.; Project administration: B.N.S.; Resources: I.K.; Visualization: B.N.S.; Writing—original draft: B.N.S.; Writing—review and editing: U.G., C.K., D.L., I.K., E.P., A.C., A.G., L.A., C.P.N., M.S., G.G.

Competing interests statement

The authors declare that no competing interests exist.

Data availability statement

The extracted data are available at https://osf.io/n2x9r.

Funding statement

This project was funded by internal funds of Cochrane Austria. Cochrane Austria is funded by a grant from the Lower Austrian Health and Social Fund (NÖGUS) and supported by the University for Continuing Education Krems.

Supplementary material

To view supplementary material for this article, please visit http://doi.org/10.1017/rsm.2025.10018.

Footnotes

This article was awarded Open Data badge for transparent practices. See the Data availability statement for details.

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Figure 0

Figure 1 PRISMA 2020 Flow diagram.

Figure 1

Table 1 Characteristics of included rapid reviews

Figure 2

Table 2 Details of rapid reviews with reported knowledge user involvement

Figure 3

Table 3 Involvement of knowledge users during the rapid review process

Figure 4

Figure 2 Reported knowledge user involvement per subgroup.

Figure 5

Figure 3 Knowledge user involvement per subgroup (main analysis vs. sensitivity analysis).

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