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Background Designing implementation interventions to change the behaviour of healthcare providers and other professionals in the health system requires detailed specification of the behaviour(s) targeted for change to ensure alignment between intervention components and measured outcomes. Detailed behaviour specification can help to clarify evidence-practice gaps, clarify who needs to do what differently, identify modifiable barriers and enablers, design interventions to address these and ultimately provides an indicator of what to measure to evaluate an intervention’s effect on behaviour change. An existing behaviour specification framework proposes four domains (Target, Action, Context, Time; TACT), but insufficiently clarifies who is performing the behaviour (i.e. the Actor). Specifying the Actor is especially important in healthcare settings characterised by multiple behaviours performed by multiple different people. We propose and describe an extension and re-ordering of TACT to enhance its utility to implementation intervention designers, practitioners and trialists: the Action, Actor, Context, Target, Time (AACTT) framework. We aim to demonstrate its application across key steps of implementation research and to provide tools for its use in practice to clarify the behaviours of stakeholders across multiple levels of the healthcare system. Methods and results We used French et al.’s four-step implementation process model to describe the potential applications of the AACTT framework for (a) clarifying who needs to do what differently, (b) identifying barriers and enablers, (c) selecting fit-for-purpose intervention strategies and components and (d) evaluating implementation interventions. Conclusions Describing and detailing behaviour using the AACTT framework may help to enhance measurement of theoretical constructs, inform development of topic guides and questionnaires, enhance the design of implementation interventions and clarify outcome measurement for evaluating implementation interventions.

Physicians often feel pressured by peers, patients and the broader medical culture to intervene, even when treatment is unnecessary.5 Patients may have high expectations, often shaped by unreliable health information.6 Additionally, healthcare systems include incentives that promote low-value care—such as the payment system and the aggressive marketing strategies of the pharmaceutical and medical device industries—which help sustain a ‘more is better’ culture.7 Since the launch of Choosing Wisely in 2012, there has been a modest but growing number of studies on de-implementation. An evidence synthesis found that the majority of barriers to de-implementation occur at the provider level, including attitudes, knowledge, skills and behaviours.10 Still, physicians frequently assert that patients seek reassurance, yet research has shown that additional tests do not necessarily provide reassurance.11 They also point to system-level factors, such as fee-for-service payment models and the medical-industrial complex.7 While these factors are important, the many successful de-implementation initiatives within current systems suggest that reducing low-value care is achievable within any system. [...]healthcare has a relatively limited impact on overall health compared with external factors such as socioeconomic conditions, individual behaviours—such as lifestyle choices—and genetic predisposition.17 It is essential that we emphasise the importance of lifestyle improvements, poverty reduction and social connectedness reducing loneliness alongside healthcare interventions. 3. Through this initiative, hospitals across the country joined a coordinated effort to curb low-value testing, allowing laboratory resources to be used more effectively.18 By sharing data, experiences and challenges in a safe environment, effective solutions can be disseminated from one region or country to another.

Correspondence to Dr Andrea M Patey, Centre for Implementation Research, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6, Canada; apatey@ohri.ca Low-value care refers to tests or treatments for which there is little evidence of benefit or more harm than benefit, which can result in poor patient outcomes such as unwarranted secondary tests or adverse events. About 25%–30% of all care has been estimated to be of low value in countries such as Australia, Canada, Spain, Brazil and the USA, and this estimate rises to 80% for certain procedures.1 There is increasing interest to identify areas of low-value care based on available evidence, guidelines and expert opinion,2 including initiatives such as Choosing Wisely3 and the British Medical Journal’s Too Much Medicine campaign.4 These campaigns aim to reduce or stop low-value services (i.e., de-implement) from the ‘bottom up’, at micro or meso levels. The authors acknowledge that the EBI programme, a complex intervention in itself, has a distinct combination of components which includes target setting for the 17 low-value procedures by each local commissioning organisation, introducing a zero tariff for certain interventions, asking all commissioning groups to implement a prior approval process for low-value interventions, monitoring agreed targets, and providing feedback to hospitals and commissioning groups. [...]this approach positions financial cost savings for the system as the predominant motivator to reduce low-value care where there may be other worthy potential targets to change clinical practice behaviours.

Background Decreasing ineffective or harmful healthcare practices (de-implementation) may require different approaches than those used to promote uptake of effective practices (implementation). Few psychological theories differentiate between processes involved in decreasing, versus increasing, behaviour. However, it is unknown whether implementation and de-implementation interventions already use different approaches. We used the behaviour change technique (BCT) taxonomy (version 1) (which includes 93 BCTs organised into 12 groupings) to investigate whether implementation and de-implementation interventions for clinician behaviour change use different BCTs. Methods Intervention descriptions in 181 articles from three systematic reviews in the Cochrane Library were coded for (a) implementation versus de-implementation and (b) intervention content (BCTs) using the BCT taxonomy (v1). BCT frequencies were calculated and compared using Pearson’s chi-squared ( χ 2 ), Yates’ continuity correction and Fisher’s exact test, where appropriate. Identified BCTs were ranked according to frequency and rankings for de-implementation versus implementation interventions were compared and described. Results Twenty-nine and 25 BCTs were identified in implementation and de-implementation interventions respectively. Feedback on behaviour was identified more frequently in implementation than de-implementation ( Χ 2 (2, n =178) = 15.693, p = .000057). Three BCTs were identified more frequently in de-implementation than implementation: Behaviour substitution ( Χ 2 (2, n =178) = 14.561, p = .0001; Yates’ continuity correction); Monitoring of behaviour by others without feedback ( Χ 2 (2, n =178) = 16.187, p = .000057; Yates’ continuity correction); and Restructuring social environment ( p = .000273; Fisher’s 2-sided exact test). Conclusions There were some significant differences between BCTs reported in implementation and de-implementation interventions suggesting that researchers may have implicit theories about different BCTs required for de-implementation and implementation. These findings do not imply that the BCTs identified as targeting implementation or de-implementation are effective, rather simply that they were more frequently used. These findings require replication for a wider range of clinical behaviours. The continued accumulation of additional knowledge and evidence into whether implementation and de-implementation is different will serve to better inform researchers and, subsequently, improve methods for intervention design.

Background Implementing new practices requires changes in the behaviour of relevant actors, and this is facilitated by understanding of the determinants of current and desired behaviours. The Theoretical Domains Framework (TDF) was developed by a collaboration of behavioural scientists and implementation researchers who identified theories relevant to implementation and grouped constructs from these theories into domains. The collaboration aimed to provide a comprehensive, theory-informed approach to identify determinants of behaviour. The first version was published in 2005, and a subsequent version following a validation exercise was published in 2012. This guide offers practical guidance for those who wish to apply the TDF to assess implementation problems and support intervention design. It presents a brief rationale for using a theoretical approach to investigate and address implementation problems, summarises the TDF and its development, and describes how to apply the TDF to achieve implementation objectives. Examples from the implementation research literature are presented to illustrate relevant methods and practical considerations. Methods Researchers from Canada, the UK and Australia attended a 3-day meeting in December 2012 to build an international collaboration among researchers and decision-makers interested in the advancing use of the TDF. The participants were experienced in using the TDF to assess implementation problems, design interventions, and/or understand change processes. This guide is an output of the meeting and also draws on the authors’ collective experience. Examples from the implementation research literature judged by authors to be representative of specific applications of the TDF are included in this guide. Results We explain and illustrate methods, with a focus on qualitative approaches, for selecting and specifying target behaviours key to implementation, selecting the study design, deciding the sampling strategy, developing study materials, collecting and analysing data, and reporting findings of TDF-based studies. Areas for development include methods for triangulating data, e.g. from interviews, questionnaires and observation and methods for designing interventions based on TDF-based problem analysis. Conclusions We offer this guide to the implementation community to assist in the application of the TDF to achieve implementation objectives. Benefits of using the TDF include the provision of a theoretical basis for implementation studies, good coverage of potential reasons for slow diffusion of evidence into practice and a method for progressing from theory-based investigation to intervention.

Choosing Wisely (CW) campaigns globally have focused attention on the need to reduce low-value care, which can represent up to 30% of the costs of healthcare. Despite early enthusiasm for the CW initiative, few large-scale changes in rates of low-value care have been reported since the launch of these campaigns. Recent commentaries suggest that the focus of the campaign should be on implementation of evidence-based strategies to effectively reduce low-value care. This paper describes the Choosing Wisely De-Implementation Framework (CWDIF), a novel framework that builds on previous work in the field of implementation science and proposes a comprehensive approach to systematically reduce low-value care in both hospital and community settings and advance the science of de-implementation.The CWDIF consists of five phases: Phase 0, identification of potential areas of low-value healthcare; Phase 1, identification of local priorities for implementation of CW recommendations; Phase 2, identification of barriers to implementing CW recommendations and potential interventions to overcome these; Phase 3, rigorous evaluations of CW implementation programmes; Phase 4, spread of effective CW implementation programmes. We provide a worked example of applying the CWDIF to develop and evaluate an implementation programme to reduce unnecessary preoperative testing in healthy patients undergoing low-risk surgeries and to further develop the evidence base to reduce low-value care.

Background Implementing evidence-based care requires healthcare practitioners to do less of some things (de-implementation) and more of others (implementation). Variations in effectiveness of behaviour change interventions may result from failure to consider a distinction between approaches by which behaviour increases and decreases in frequency. The distinction is not well represented in methods for designing interventions. This review aimed to identify whether there is a theoretical rationale to support this distinction. Methods Using Critical Interpretative Synthesis, this conceptual review included papers from a broad range of fields (biology, psychology, education, business) likely to report approaches for increasing or decreasing behaviour. Articles were identified from databases using search terms related to theory and behaviour change. Articles reporting changes in frequency of behaviour and explicit use of theory were included. Data extracted were direction of behaviour change, how theory was operationalised, and theory-based recommendations for behaviour change. Analyses of extracted data were conducted iteratively and involved inductive coding and critical exploration of ideas and purposive sampling of additional papers to explore theoretical concepts in greater detail. Results Critical analysis of 66 papers and their theoretical sources identified three key findings: (1) 9 of the 15 behavioural theories identified do not distinguish between implementation and de-implementation (5 theories were applied to only implementation or de-implementation, not both); (2) a common strategy for decreasing frequency was substituting one behaviour with another. No theoretical basis for this strategy was articulated, nor were methods proposed for selecting appropriate substitute behaviours; (3) Operant Learning Theory makes an explicit distinction between techniques for increasing and decreasing frequency. Discussion Behavioural theories provide little insight into the distinction between implementation and de-implementation. Operant Learning Theory identified different strategies for implementation and de-implementation, but these strategies may not be acceptable in health systems. Additionally, if behaviour substitution is an approach for de-implementation, further investigation may inform methods or rationale for selecting the substitute behaviour.

Evidence-based guidelines for managing LBP exist but their recommendations are often not used by health professionals in primary care. A key challenge to address this issue is understanding how people understand LBP, how they feel about it, and cope with it - particularly with regard to why they visit their doctors and their treatment expectations. This is important to understand, particularly since physician barriers to following LBP treatment guidelines have centered on patient issues (such as patient demand for imaging). This was a qualitative, exploratory study using semi-structured interviews to explore patient perceptions of LBP and their coping strategies, paying particular attention to why patients with LBP in Newfoundland and Labrador (NL) seek care from family physicians and their treatment expectations, especially with regard to imaging. Eligible patients included adults aged 18 + years or older, living in both rural and urban settings in NL, Canada, who had visited their family physician about low back pain within the year prior to the interview. Researchers experienced in applying the Common-sense Model of Self-regulation (CSM), used the model to inform the development of our question guide and as a framework for the data analysis. We found that new onset, severity, or persistent pain prompted patients to visit their family doctor, primarily to seek advice and/or a diagnosis, or for a referral to imaging or other providers. While patients believed that imaging was essential to understanding the underlying cause of their symptoms or informing their treatment, they were divided about its effectiveness - some felt it was beneficial to their treatment while others reported that it had no effect. We found that patients were unified in their largely negative views regarding prognosis and all experienced a range of negative emotions surrounding their LBP such as fear, stress, frustration, and guilt. We also found wide variation in understanding of cause and use of coping strategies. Patients posited several causes for the pain including injury, overexertion, comorbid conditions, and issues related to posture and sitting, and were split on their thoughts regarding prevention - about half thought it could be prevented, half did not. We found that patients coped with their LBP using a variety of strategies but were often disappointed in the results. Most reported no benefit to visiting their family doctors for their LBP. Some were pleased with their experiences with allied HCPs, noting small, but steady, improvements using recommended exercises but others were generally dissatisfied. Our exploration of patient views and expectations for low back pain care indicates a mismatch between the care they are looking for and the care they receive. It also suggested a general lack of knowledge about the cause of LBP, the value and usefulness of imaging for its diagnosis and treatment, and poor physician-patient communication.

Background Routine pre-operative tests for anesthesia management are often ordered by both anesthesiologists and surgeons for healthy patients undergoing low-risk surgery. The Theoretical Domains Framework (TDF) was developed to investigate determinants of behaviour and identify potential behaviour change interventions. In this study, the TDF is used to explore anaesthesiologists’ and surgeons’ perceptions of ordering routine tests for healthy patients undergoing low-risk surgery. Methods Sixteen clinicians (eleven anesthesiologists and five surgeons) throughout Ontario were recruited. An interview guide based on the TDF was developed to identify beliefs about pre-operative testing practices. Content analysis of physicians’ statements into the relevant theoretical domains was performed. Specific beliefs were identified by grouping similar utterances of the interview participants. Relevant domains were identified by noting the frequencies of the beliefs reported, presence of conflicting beliefs, and perceived influence on the performance of the behaviour under investigation. Results Seven of the twelve domains were identified as likely relevant to changing clinicians’ behaviour about pre-operative test ordering for anesthesia management. Key beliefs were identified within these domains including: conflicting comments about who was responsible for the test-ordering (Social/professional role and identity); inability to cancel tests ordered by fellow physicians (Beliefs about capabilities and social influences); and the problem with tests being completed before the anesthesiologists see the patient (Beliefs about capabilities and Environmental context and resources). Often, tests were ordered by an anesthesiologist based on who may be the attending anesthesiologist on the day of surgery while surgeons ordered tests they thought anesthesiologists may need (Social influences). There were also conflicting comments about the potential consequences associated with reducing testing, from negative (delay or cancel patients’ surgeries), to indifference (little or no change in patient outcomes), to positive (save money, avoid unnecessary investigations) (Beliefs about consequences). Further, while most agreed that they are motivated to reduce ordering unnecessary tests (Motivation and goals), there was still a report of a gap between their motivation and practice (Behavioural regulation). Conclusion We identified key factors that anesthesiologists and surgeons believe influence whether they order pre-operative tests routinely for anesthesia management for a healthy adults undergoing low-risk surgery. These beliefs identify potential individual, team, and organisation targets for behaviour change interventions to reduce unnecessary routine test ordering.

Background Promoting the uptake of vaccination for infectious diseases such as COVID-19 remains a global challenge, necessitating collaborative efforts between public health units (PHUs) and communities. Applied behavioural science can play a crucial role in supporting PHUs’ response by providing insights into human behaviour and informing tailored strategies to enhance vaccination uptake. Community engagement can help broaden the reach of behavioural science research by involving a more diverse range of populations and ensuring that strategies better represent the needs of specific communities. We developed and applied an approach to conducting community-based behavioural science research with ethnically and socioeconomically diverse populations to guide PHUs in tailoring their strategies to promote COVID-19 vaccination. This paper presents the community engagement methodology and the lessons learned in applying the methodology. Methods The community engagement methodology was developed based on integrated knowledge translation (iKT) and community-based participatory research (CBPR) principles. The study involved collaboration with PHUs and local communities in Ontario, Canada to identify priority groups for COVID-19 vaccination, understand factors influencing vaccine uptake and co-design strategies tailored to each community to promote vaccination. Community engagement was conducted across three large urban regions with individuals from Eastern European communities, African, Black, and Caribbean communities and low socioeconomic neighbourhoods. Results We developed and applied a seven-step methodology for conducting community-based behavioural science research: (1) aligning goals with system-level partners; (2) engaging with PHUs to understand priorities; (3) understanding community strengths and dynamics; (4) building relationships with each community; (5) establishing partnerships (community advisory groups); (6) involving community members in the research process; and (7) feeding back and interpreting research findings. Research partnerships were successfully established with members of prioritized communities, enabling recruitment of participants for theory-informed behavioural science interviews, interpretation of findings, and co-design of targeted recommendations for each PHU to improve COVID-19 vaccination uptake. Lessons learned include the importance of cultural sensitivity and awareness of sociopolitical context in tailoring community engagement, being agile to address the diverse and evolving priorities of PHUs, and building trust to achieve effective community engagement. Conclusion Effective community engagement in behavioural science research can lead to more inclusive and representative research. The community engagement approach developed and applied in this study acknowledges the diversity of communities, recognizes the central role of PHUs, and can help in addressing complex public health challenges.