Over the past two decades, initiatives promoting research-policy engagement have increased broadly and in health. Numerous factors influencing the engagement of policymakers in research have been described primarily from the perspective of researchers. This scoping review aimed to identify the enablers and barriers to policymaker engagement across the research process from the perspective of policymakers.

Scoping review following the Joanna Briggs Institute Methods Manual for scoping reviews.

MEDLINE, Cochrane Library, Social Policy and Practice, Campbell Collaboration, Health Systems Evidence and World Bank e-Library, supplemented by grey literature from Google Scholar, WHO Global Index Medicus and VHL Regional Portal.

We included English language studies published after 2007 that involved policymakers at national or subnational levels who were actively engaged in research at any stage. We excluded studies which did not include policymakers, where engagement was passive, or perspectives were marginal or not clearly outlined.

After screening and full-text review, we extracted and coded data using MAXQDA Plus 24. We conducted thematic analysis, categorising findings as enablers or barriers into three levels: individual, organisational and contextual/system. Findings were iteratively reviewed and refined by the research team.

We screened 5384 titles and abstracts, reviewed 59 full-text documents and included 30 articles for analysis. Most studies were published after 2016 and were focused on policymaker engagement at the national level. Organisational factors were the most frequently reported influences on engagement of policymakers in research across different contexts. The most frequent enablers mentioned in the literature were (1) the institutionalisation of partnerships, initiatives and having formal agreements; (2) defining goals, roles, responsibilities and conflict resolution mechanisms; (3) researchers providing practical and expert advice to policymakers; (4) leveraging networks; and (5) having supportive institutions. The most frequent barriers were (1) the lack of regulations, infrastructure, funding and communication channels to support engagement; (2) the lack of skills of researchers to understand policymaking processes and work in collaboration with policymakers; and (3) the mismatch in priorities, values, perspectives and expectations.

Our study highlights the role of institutional support, widespread collaboration opportunities and the interconnected nature of these factors within the research-policy ecosystem.

Open Science Framework (https://osf.io/ynr78/).

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a high 5-year survival rate of approximately 98%. Despite advances in diagnosis and treatment, up to 20% of patients experience recurrence, adversely affecting their quality of life. Predictive models have been developed to assess recurrence risk and guide clinical decision-making, but these models often face limitations such as retrospective design, lack of diversity in study populations and absence of external validation. The primary aim is to externally validate existing predictive models for DTC recurrence using prospective data from a diverse Latin American cohort. The secondary aim is to explore opportunities for model recalibration to improve their performance in our population.

The CaTaLiNA study is a multicentre prospective observational study conducted across 10 hospitals in five Latin American countries, including Ecuador, Peru, Uruguay and Mexico. Patients aged 18 years or older receiving treatment for DTC, such as the first thyroid surgery, active surveillance or radiofrequency ablation will be included. Recruitment will occur from November 2023 to June 2025, with follow-up extending until June 2028. Data collection will include baseline clinical, surgical and histological characteristics, treatment details and follow-up outcomes. Statistical analysis will follow the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines, using imputation strategies for missing data and evaluating calibration and discrimination of the prediction models. Calibration measures include the ratio of expected and observed events, calibration slope and calibration plot, while discrimination will be assessed using the C-index and area under the receiver operating characteristic curve.

This study protocol was approved by Comité de Ética de Investigación en Seres Humanos de la Universidad San Francisco de Quito USFQ ‘CEISH-USFQ’ APO-010–2023-CEIHS-USFQ Oficio No. 161-2023-CA-23030M-CEISH-USFQ. Results will be disseminated via peer-reviewed publications.