Patient engagement in health implementation research: A logic model

Abstract

IntroductionGrowing evidence supports patient engagement (PE) in health implementation research to improve the quality, relevance and uptake of research. However, more guidance is needed to plan and operationalize PE before and throughout the research process. The aim of the study was to develop a logic model illustrating the causal links between context, resources, activities, outcomes and impact of PE in an implementation research programme.MethodsThe Patient Engagement in Health Implementation Research Logic Model (hereafter the Logic Model) was developed using a descriptive qualitative design with a participatory approach, in the context of the PriCARE programme. This programme aims to implement and evaluate case management for individuals who frequently use healthcare services in primary care clinics across five Canadian provinces. Participant observation of team meetings was performed by all team members involved in the programme and in‐depth interviews were conducted by two external research assistants with team members (n = 22). A deductive thematic analysis using components of logic models as coding categories was conducted. Data were pooled in the first version of the Logic Model, which was refined in research team meetings with patient partners. The final version was validated by all team members.ResultsThe Logic Model highlights the importance of integrating PE into the project before its commencement, with appropriate support in terms of funding and time allocation. The governance structure and leadership of both principal investigators and patient partners have significant effects on PE activities and outcomes. As an empirical and standardized illustration that facilitates a shared understanding, the Logic Model provides guidance for maximizing the impact of patient partnership in various contexts for research, patients, providers and health care.ConclusionThe Logic Model will help academic researchers, decision makers and patient partners plan, operationalize, and assess PE in implementation research for optimal outcomes.Patient or Public ContributionPatient partners from the PriCARE research programme contributed to developing the research objectives and designing, developing and validating data collection tools, producing data, developing and validating the Logic Model and reviewing the manuscript.

Beyond Participation: Evaluating the Role of Patients in Designing Oncology Clinical Trials

“…Patient involvement in clinical trials encounters several significant challenges, including logistical issues such as the time and resources required to facilitate in-depth patient engagement, alongside a scientific discord between medical experts and lay perspectives on research [ 43 , 44 ]. Other challenges involve integrating patient insights into the traditional hierarchical structure of scientific evidence while ensuring a diverse and representative patient sample [ 43 , 44 , 45 , 46 ]. Various strategies can be used to address these challenges.…”

Logical model of reception and risk classification for women with pre-eclampsia and eclampsia

“…The evaluation of a health intervention program allows monitoring of the actions developed, identification of necessary changes and judgment of success in achieving results. To develop an evaluation, it is necessary to understand the structure of the program and correlate the available resources and assistance actions with the expected results ( 5 , 6 ) .…”

“…The second concerns the pre-assembly of the logical model and can be represented by the problem tree technique, which includes information about the problem, the intervention, general objective, target audience and beneficiaries. The third deals with the validation of the model, which in this case occurred through judgment by stakeholders ( 5 , 6 ) .…”

“…The participation of professionals interested in the program, also known as stakeholders, in the validation of the LM is essential for different perceptions to be raised about each topic covered, in addition to reaching consensus among those involved. The interaction and agreement between stakeholders represent a factor for LM to operationalize assessments that truly represent the needs of internal and external users of assistance ( 5 , 6 ) .…”

“…It is also noteworthy that the description of the components of the ACCR for women with pre-eclampsia/eclampsia, through modeling, will allow the recognition of its functioning, favoring the production of useful information for care practice and management ( 6 ) , indicating that Validation of the ML may contribute to supporting strategies for qualifying the care process. The objective of this study was to describe the validation of the Logical Model of Reception and Risk Classification for women with pre-eclampsia/eclampsia in a maternity hospital in the city of Rio de Janeiro.…”