Background: Despite wide recognition that clinical care should be informed by the best available evidence, this does not always occur. Despite a myriad of theories, models and frameworks to promote evidence-based population health, there is still a long way to go, particularly in maternity care. The aim of this study is to appraise the scientific study of methods to promote the systematic uptake of evidence-based interventions in maternity care. This is achieved by clarifying if and how implementation science theories, models, and frameworks are used. Methods: To map relevant literature, a scoping review was conducted of articles published between January 2005 and December 2019, guided by Peters and colleagues’ (2015) approach. Specifically, the following academic databases were systematically searched to identify publications that presented findings on implementation science or the implementation process (rather than just the intervention effect): Business Source Complete; CINAHL Plus with Full Text; Health Business Elite; Health Source: Nursing/Academic Edition; Medline; PsycARTICLES; PsycINFO; and PubMed. Information about each study was extracted using a purposely designed data extraction form. Results: Of the 1181 publications identified, 158 were included in this review. Most of these reported on factors that enabled implementation, including knowledge, training, service provider motivation, effective multilevel coordination, leadership and effective communication—yet there was limited expressed use of a theory, model or framework to guide implementation. Of the 158 publications, 144 solely reported on factors that helped and/or hindered implementation, while only 14 reported the use of a theory, model and/or framework. When a theory, model or framework was used, it typically guided data analysis or, to a lesser extent, the development of data collection tools—rather than for instance, the design of the study. Conclusion: Given that models and frameworks can help to describe phenomenon, and theories can help to both describe and explain it, evidence-based maternity care might be promoted via the greater expressed use of these to ultimately inform implementation science. Specifically, advancing evidence-based maternity care, worldwide, will require the academic community to make greater explicit and judicious use of theories, models, and frameworks. Registration: Registered with the Joanna Briggs Institute (registration number not provided).

Proliferation of terms describing the science of effectively promoting and supporting the use of research evidence in healthcare policy and practice has hampered understanding and development of the field. To address this, an international Terminology Working Group developed and published a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. This paper presents results of validation work and a second international workgroup meeting, culminating in the updated AIMD framework [Aims, Ingredients, Mechanism, Delivery]. Framework validity was evaluated against terminology schemas (n = 51); primary studies (n = 37); and reporting guidelines (n = 10). Framework components were independently categorized as fully represented, partly represented, or absent by two researchers. Opportunities to refine the framework were systematically recorded. A meeting of the expanded international Terminology Working Group updated the framework by reviewing and deliberating upon validation findings and refinement proposals. There was variation in representativeness of the components across the three types of literature, in particular for the component ‘causal mechanisms’. Analysis of primary studies revealed that representativeness of this concept lowered from 92 to 68% if only explicit, rather than explicit and non-explicit references to causal mechanisms were included. All components were very well represented in reporting guidelines, however the level of description of these was lower than in other types of literature. Twelve opportunities were identified to improve the framework, 9 of which were operationalized at the meeting. The updated AIMD framework comprises four components: (1) Aims: what do you want your intervention to achieve and for whom? (2) Ingredients: what comprises the intervention? (3) Mechanisms: how do you propose the intervention will work? and (4) Delivery: how will you deliver the intervention? The draft simplified framework was validated with reference to a wide range of relevant literature and improvements have enhanced useability. The AIMD framework could aid in the promotion of evidence into practice, remove barriers to understanding how interventions work, enhance communication of interventions and support knowledge synthesis. Future work needs to focus on developing and testing resources and educational initiatives to optimize use of the AIMD framework in collaboration with relevant end-user groups.

Purpose: – The purpose of this paper is to provide a retrospective analysis of computer simulation’s role in accelerating individual innovation adoption decisions. The process innovation examined is Lean Systems Thinking, and the organizational context is the imaging department of an Australian public hospital.Design/methodology/approach: – Intrinsic case study methods including observation, interviews with radiology and emergency personnel about scheduling procedures, mapping patient appointment processes and document analysis were used over three years and then complemented with retrospective interviews with key hospital staff. The multiple data sources and methods were combined in a pragmatic and reflexive manner to explore an extreme case that provides potential to act as an instructive template for effective change.Findings: – Computer simulation of process change ideas offered by staff to improve patient-flow accelerated the adoption of the process changes, largely because animated computer simulation permitted experimentation (trialability), provided observable predictions of change results (observability) and minimized perceived risk.Research limitations/implications: – The difficulty of making accurate comparisons between time periods in a health care setting is acknowledged.Practical implications: – This work has implications for policy, practice and theory, particularly for inducing the rapid diffusion of process innovations to address challenges facing health service organizations and national health systems.Originality/value: – The research demonstrates the value of animated computer simulation in presenting the need for change, identifying options, and predicting change outcomes and is the first work to indicate the importance of trialability, observability and risk reduction in individual adoption decisions in health services.

Background: To better understand and promote public health, participatory research with Indigenous peoples represents recommended practice, worldwide. However, due to the different ways such research is referred to, described, and used, it is unclear what might (and might not) warrant the term when collaborating with Indigenous peoples. As such, this article expands conceptual understandings of participatory research with Indigenous peoples, across timelines and regions.Method: Following a systematic search of 29 academic databases in April 2018, a lexical analysis of the methods sections was conducted, which were sourced from 161 publications across 107 journals.Results: The active involvement of Indigenous peoples in research that is expressly participatory is limited across all project phases. This might be because the ways in which Indigenous peoples were involved throughout were not reported – however, it might also be because Indigenous peoples were not involved in all project phases. Furthermore, descriptions differ by study location and publication timeframe – notably, studies in the region of the Americas chiefly refer to pandemics, surveyors, and art; and those published in the last two decades have given primacy to artifacts of interest.Conclusions: Findings from this corpus of data suggest participatory research with Indigenous peoples is not always described across different project phases; furthermore, it differs according to study location and publication timeframe. This offers considerable opportunity to further this important research area via alternative methodologies that award primacy to Indigenous expertise and agency.