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Specifying behavioural and strategy components of de-implementation efforts targeting low-value prescribing practices in secondary health care

Implementation Science Communications volume 5, Article number: 88 (2024) Cite this article

Abstract

Background

/Aims

De-implementation, including the removal or reduction of unnecessary or inappropriate prescribing, is crucial to ensure patients receive appropriate evidence-based health care. The utilization of de-implementation efforts is contingent on the quality of strategy reporting. To further understand effective ways to de-implement medical practices, specification of behavioural targets and components of de-implementation strategies are required. This paper aims to critically analyse how well the behavioural targets and strategy components, in studies that focused on de-implementing unnecessary or inappropriate prescribing in secondary healthcare settings, were reported.

Methods

A supplementary analysis of studies included in a recently published review of de-implementation studies was conducted. Article text was coded verbatim to two established specification frameworks. Behavioural components were coded deductively to the five elements of the Action, Actor, Context, Target, Time (AACTT) framework. Strategy components were mapped to the nine elements of the Proctor’s ‘measuring implementation strategies’ framework.

Results

The behavioural components of low-value prescribing, as coded to the AACTT framework, were generally specified well. However, the Actor and Time components were often vague or not well reported. Specification of strategy components, as coded to the Proctor framework, were less well reported. Proctor’s Actor, Action target: specifying targets, Dose and Justification elements were not well reported or varied in the amount of detail offered. We also offer suggestions of additional specifications to make, such as the ‘interactions’ participants have with a strategy.

Conclusion

Specification of behavioural targets and components of de-implementation strategies for prescribing practices can be accommodated by the AACTT and Proctor frameworks when used in conjunction. These essential details are required to understand, replicate and successfully de-implement unnecessary or inappropriate prescribing. In general, standardisation in the reporting quality of these components is required to replicate any de-implementation efforts.

Trial registration

Not registered.

Peer Review reports

Introduction

Many complex behavioural interventions, or strategies, are embedded in healthcare to ensure the delivery of high-quality and cost-efficient care practices [1]. Behaviour change strategies which aim to de-implement (i.e. reduce or remove) a behaviour, as opposed to implement one, have gained traction over recent years as an approach to ensure evidence-based health care [2]. However effective contributions to evidence-based practice are contingent on the quality of de-implementation strategy reporting [3].

Insufficient reporting of behaviour change strategies has been a long-standing issue in implementation and de-implementation alike [4, 5]. Extensive research has been undertaken to understand the difference between implementation and de-implementation [6,7,8,9] and the unique strategies required for de-implementation [3, 10,11,12]. However, in order to effectively tailor, replicate or scale up these efforts, full and precise reporting of the distinct strategies and behaviours used in de-implementation are required [13, 14]. Implementation and behaviour change science provides a platform to gauge the quality of behaviour change reporting. Multiple frameworks have been developed to capture the necessary information of how and why strategies were produced [15,16,17] and the target behaviour of interest, i.e. ‘who’ has to do ‘what’ [18,19,20].

Specifying behaviour as ‘who’ does ‘what’ and ‘when’ [19], has been further developed to capture important features of health professional behaviour. The Action, Actor, Context, Target, Time (AACTT) framework [20] consists of five elements to specify a target behaviour: the Action (the discrete activity of focus), the Actor (the healthcare professional who does the action), Context (the environment or situation in which the action happens), Time (when the action takes place) and Target (who the action regards).

The Proctor framework [17] offers guidance on salient information that should be offered when reporting an implementation (or de-implementation) strategy. This framework offers a ‘How to’ guide to help reproduce the strategy through nine elements: Name it (the title of the strategy), Define it (description the strategy and content), Action (the processes that take place for the strategy to be enacted), Actor (who does the action), Action target (the strategy targets, including the unit of analysis), Temporality (when the strategy is used), Dose (the intensity of the strategy), Implementation outcome affected (definition and measurement of implementation outcomes) and Justification (the reasons for the selection of the strategy and its content).

The application of these frameworks, used for good reporting practice, in combination aids the understanding of the quality of de-implementation strategy and target behaviour reporting.

De-implementation has a valuable role to facilitate evidence-based practice [2, 8] but can only be utilized and improved upon where essential information is offered [3]. This approach ensures a comprehensive analysis of the reporting of de-implementation to understand where improvements in reporting are required. This study aimed to examine how well behavioural targets and the components of de-implementation strategies, addressing inappropriate prescribing in secondary care settings, were reported.

Methods

Design

This was a supplementary analysis [21] of 11 randomised control trials included in a recent systematic review which evaluated behaviour change strategies used to de-implement low-value medication prescribing in secondary care [3]. Details about the search strategy, study selection, risk of bias and synthesis of results can be found in the original review. The review reported on the effectiveness, the barriers and facilitators and unintended consequences of de-implementation [3].

Data extraction

A bespoke data extraction form incorporated the five elements of the AACTT framework [20] and the nine Proctor framework elements [17]. Definitions are summarised in Table 1. The AACTT framework was applied to the target behaviour that the strategy attempted to change. The Proctor framework was applied to the strategies that were delivered. A coding manual was created with definitions for each element of the frameworks and discussed with the research team (ED, SM, SJM) and revised iteratively. Coding suggestions, derived from our coding progress, can be seen in Table 2. To ensure interpretations of framework definitions were systematic, a second coder (ED) double-coded 45% of the studies. Disagreements in coding were resolved by discussion with the research team and definition interpretations were reviewed.

Table 1 Definitions for the AACTT and proctor frameworks
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Table 2 Suggestions for using the AACTT and Proctor Frameworks for specifying de-implementation
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Data analysis

Data was coded deductively to each framework. The verbatim text was extracted to ensure detail was captured. Characteristics of included studies; behaviour targets and strategy components were tabulated. Strategies were classified to the well-established Effective Practice and Organisation of Care (EPOC) taxonomy to allow for comparison [33].

Results of the review

Study characteristics can be found in Table 3. Reminder strategies were the most common (8/11 studies) [22, 24,25,26,27,28, 31, 34], education materials (4 studies) [24, 26, 30, 32] and Audit and feedback (3 studies) [26, 30, 32] strategies were the next most common. The low-value prescribing practice (i.e. the behavioural target) included inappropriate antibiotics for a range of illnesses (6 studies) [22, 26, 28, 30, 32, 34], and inappropriate drug prescriptions for the treatment of malaria, renal impairment and of older adults (5 studies) [23,24,25, 27, 31]. Two strategies included content targeting the patient [26, 32].

Table 3 Interventions EPOC classification, focus, type and reported effectiveness
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Eight studies compared their strategies to a usual care control group [22, 23, 25, 27, 28, 31, 32, 34]. Three studies offered a partial or adapted strategy [24, 26, 30]. The reporting of framework elements for control groups can be found in Additional file 1 and 2. Effectiveness results are reported elsewhere [3].

Specification of behaviour using the AACTT framework

Table 4. shows the AACTT elements reported for each study. Full verbatim coding can be found in Additional File 3. Elements of AACTT; Action, Context and Target were reported well. The Action was reported for all studies, mostly reported as part of the main outcome (e.g., reduce inappropriate prescribing). Contextual information relating to the physical context including the clinical setting, location and the capacity of location were identified in all studies. Studies were conducted in a range of countries and the majority were in high-income countries [22, 24,25,26,27,28, 31, 32, 34]. Studies were set in emergency departments or urgent care units [22, 25, 26, 31, 32], other ward types [24, 27, 28] or whole hospitals [23, 30, 34].

Table 4. Reported AACTT framework domains
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The Targets in these studies were the patients. Adults [25, 28, 32, 34], children [22, 30], elderly [24, 31] and a mix of children and adult patients [23, 26, 27] were reported. Actor and Time elements were underreported and are discussed in further detail.

Reporting of actor

All studies, bar one [23], specified an actor. “Physician” was the most reported type of Actor [22, 24, 25, 27, 28, 31, 34], however, other unspecific terms such as “provider” [26] or “clinician” [32] were also reported. Opondo and colleagues (2011) referred to different staff members for four of the seven components of their strategy [30]. Menya and colleagues (2015) did not report an actor, their Pay for Performance incentive strategy was rolled out at the facility level and it was not clear which staff members had to change their behaviour for the incentives to be offered [23].

Reporting of time

Six (of 11) studies reported the Time at which the Actor performs the Action [22, 25, 27, 28, 30, 31]. The timing of the decision support strategies highlighted when the Actor was performing the Action (e.g. writing a prescription) [25, 27, 28, 31]. However, education-focused strategies did not specify when the Action was performed [24, 26, 32], except Opondo and colleagues (2011) who specified they were trying to change prescribing behaviour when the patients were admitted to the hospital [30].

Specification of strategy components to the proctor framework

Table 5 summarises the Proctor elements that were reported. Full verbatim coding can be found in Additional File 4. In all 11 studies, the strategies were named, defined, and reported as a clear unit of analysis. It should be noted that even when elements were reported, there was variation in the type or amount of information provided. For example, it was possible to specify a name for each of the strategies, but this varied from formal programme names (e.g., “MediDSS: Medilogy Decision Support System” as seen in Moja et al., 2019, p. 3), to other strategies being reported more informally (e.g., “decision support” as seen in Daley et al., 2018, p. 184).

Table 5 Reported Proctor framework domains
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The steps required to set up the strategy that were identified in the Proctor’s Action were generally well reported. Three studies [22, 25, 31] refer to consultation with experts in the design of their strategies. Paul and colleagues (2006) offered more precise details of the information required (i.e., complete patient demographics and test results) for their strategy to produce decision-support output. The Actor, Action target: specifying targets, Dose and Justification elements were varied or not well reported.

Reporting of actor

For strategies using decision support, Proctor’s Actor, defined as who implements the strategy or the strategy provider, was lacking. Where decision support may be automated and integrated into the electronic health record, a strategy provider is not always applicable or could be identified as the computerised health system [25, 27, 28, 31, 34].

For strategies that used education, Actors were better specified, and some studies specified where they sourced their Actors. Metlay and colleagues (2007) sourced a clinical leader from each site to host training sessions [32]. Opondo and colleagues (2011) listed Actors for three of seven parts in their multifaceted strategy which included a paediatrician from the study team and a local site-based facilitator [30]. Again, the amount of detail provided varied as seen in Menya and colleague's study (2015), where Actors were from the "study team" (p. 4) [23].

Reporting of Action target, specifying targets

Proctor's definition of Action Target or “Target(s) of the action” (p.6) has two parts, one part is defined as: “[Identification of a] unit of analysis for measuring implementation Outcomes” (p. 4) and the other as: “where [strategies] are directed or the conceptual ‘targets’ they attempt to impact” (p. 5) [17]. This analysis maintained the definition regarding the unit of analysis. However, extended the definition regarding the ‘conceptual targets’ into the level of participants targeted (e.g. individual, hospital) and relevant participant characteristics, in addition to the identification of conceptual targets (e.g. knowledge, social support). We distinguished the Action Target - level and characteristics element from AACTT’s Actor, by collecting the level the strategy targeted, for example: “All physicians in the participating wards" (Pg. 54) [24], indicates that individuals were the level of action target and "facility-directed" (Pg. 4) [23] meant sites were the level targeted.

All studies reported the level of participants the strategy was aiming to target, nine strategies targeted individuals [22, 24,25,26,27,28, 31, 32, 34] and two targeted facilities [23, 30]. Where possible, we also collected relevant characteristics of the participants that related to the strategy. Four studies provided a count of clinicians included in the trial [25, 26, 30, 31]. Three studies offered participants’ characteristics, Terrell and colleagues (2009) and (2010) captured gender, job status and time since training demographics. Opondo and colleagues (2011) captured gender, age, qualifications and time in their roles.

Seven (of 11) studies reported the conceptual targets the strategy attempted to change [23, 24, 26, 27, 30,31,32] to varying degrees. Franchi and colleagues (2016) reported that their education strategy attempted to: “enhance knowledge and performance” (p. 54) [24]. Whereas Menya and colleagues’ (2015) strategy attempted to: “foster cooperation between departments” (p. 4) [23]. Other studies were less specific, such as Moja and colleagues (2018) who wished to: “encourage better adherence to evidence-based guidelines” (p. 2) [27].

Reporting of dose

Five (of 11) studies specified the Dose for all or some of their strategies [23, 25,26,27, 30]. The Dose was poorly reported across different types of strategies. Two (of eight) strategies using decision support [25, 27], reported the intensity of the decision support, for example: “presented on screen when clinicians entered new information” (Moja et al., 2019, p. 3). Strategies using education components were also poorly reported [24, 26, 30, 32]. Yadav and colleagues (2019) specified a “monthly” dose and Opondo and colleagues (2011) stated a “six-monthly” dose for the audit and feedback strategies, but both failed to specify the dose for their education strategies.

Reporting of Justification

In total, nine studies (of 11) offered a Justification of why a strategy was used [22,23,24,25,26,27, 31, 32, 34]. One study took a pragmatic approach and did not offer empirical or theoretical reasoning [34]. In multiple cases [27, 34], authors were staff members in the hospital where the strategy was run, which may have informed their approach. This could have been the case for other studies, but this was less clear.

Six studies referenced empirical research only [22,23,24,25, 27, 31]. Justifications referred to empirical work as the reason for using the strategy and why the strategy would be suitable to the setting. One study referred to their own previously published work [25].

Only two studies cited established theories to inform their strategy development. Metlay and colleagues (2007) used the Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation (PRECEDE) model of behaviour change [29]. Metlay and colleagues referenced mechanisms of increasing knowledge, use of feedback and patient education in attempts to reduce antibiotic prescribing [32]. Yadav and colleagues (2019) referred to behavioural economics and decision science, and referenced mechanisms of accountability and social norms [35, 36] that they expected their strategy to impact [26]. Neither of these studies measured these mechanisms, as trial outcomes focused on effectiveness of the strategies.

Franchi and colleagues (2016), did not reference theory but referenced potential mechanisms through which they expected their strategy to work. They postulated that their education strategy would increase knowledge which, in turn, would decrease inappropriate prescribing [24].

Additional element identified: interactions

Throughout the analysis, we identified another area of relevance. The way Actors (i.e. those that were required to change) engaged or were to engage with the strategies was identified, we have named this element ‘Interactions’. Interactions were reported (or partly reported) in seven studies (of 11) [24, 25, 27, 28, 30, 31, 34] (see Table 6). The expected interactions offered details of how the strategy was to be used, for example, “Physicians were asked to inspect [the systems] interface” (Paul et al., 2006, p. 1240) or "The prescriber had the option to order a recommended alternative therapy or to reject the recommendations” (Terrell et al., 2009, p.1389). See Additional file 5 for verbatim coded text.

Table 6 Additional identified element of ‘interactions’ reported
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Franchi and colleagues set out their expectations for participants’ interactions with the educational component; "Every clinician had to finish his/her e-learning program within 1 month" (p. 54), however, they did not specify how health care professionals were to interact with the reminder component – which may have contributed to the lack of its uptake [24]. Elsewhere, some information offered was vague and the interaction with the strategy could be inferred, for example, “Reminders…presented …when clinicians entered new information” (Moja et al., 2019, p. 3).

Our suggestions for using the AACTT and Proctor frameworks, with our additional considerations, for specifying de-implementation have been collated for quick reference. See Table 2.

Discussion (interpretation of the results)

This supplementary analysis aimed to understand the quality of the reporting of the behaviour targets and de-implementation strategies from studies included in a systematic review addressing the de-implementation of low-value prescribing practices in secondary care. Behavioural targets and strategy components were coded to the AACTT and Proctor frameworks respectively, using a deductive approach. These frameworks used in conjunction allowed assessment of how well low-value care behaviours de-implementation strategies were specified. Our analysis highlighted the reported information, particularly in some key elements, to be lacking, varied or brief in detail. We also highlight another potential element of ‘Interactions’, that provided information on how Actors engage with the strategies, which we deem useful to better understand the process of de-implementation.

A key finding in this review was that elements of Actor and Time in the AACTT framework were underreported or insufficient in detail. Whereas, other elements of the AACTT framework; Action, Context and Target were more consistently reported. Actors were specified using unspecific language (e.g., “Physician” or “Clinician”) which can infer a prescribing role however, does not give any indication of the medical speciality or tenure of the Actor. In a complex health system, there are multiple Actors who are responsible for multiple patients and conduct many behaviours. The exact specification of the Actor has been recognised to be instrumental to understanding healthcare professionals’ behaviours and identifying who is required to change [20]. If an actor is not sufficiently identified, this could lead to inaction in strategy efforts [13]. Additionally, the timing of the behaviour requiring change was also lacking, particularly in studies of strategies that were conducted before the point of care. Admission and discharge were often used to measure the outcome of the prescribing rate, but it was not clear if the Action (i.e. prescribing behaviour) was happening at this time.

The insufficient reporting of AACTT’s Actor and Time elements echoes the results of a recent behavioural analysis of hospital-based antimicrobial stewardship strategies [13]. Duncan and colleagues also found Actor and Time to be underreported or unspecific. It is likely that when reporting a behaviour authors feel these components can be inferred from information about the Action or the Target of the behaviour. However, failing to offer specific details about who does the behaviour and when it happens remains problematic. Actors could refer to multiple types of staff members, who could prescribe at multiple time points. The lack of specification of the target behaviour makes it difficult to understand the low-value prescribing behaviour and the circumstances in which de-implementation strategies must work.

The Proctor framework was less consistently reported compared to the AACTT framework. Proctor’s Actor, Action target, Dose and Justification elements were not well reported. Proctor’s

Actors, or those responsible for delivering the strategy, were well-reported in studies that offered training or monitoring. However, they were underreported in studies where the strategy may not have been facilitated by a person (i.e. computerised decision support). The electronic health system was identified as the Actor in these cases. Only two of the eight strategies using decision support specified the people involved in strategy design. It can be assumed that integrating a decision support strategy into an electronic health record can be an involved process relying on software developers and IT staff. Failing to specify a strategy provider and those involved in the process of strategy design and delivery can lead to replication issues [17]. It is interesting to note that a few of the study authors were staff members of the locations where strategies were evaluated. This could mean they had tacit knowledge and/or established relationships with key IT contacts, to facilitate integration of their strategy into the electronic health record.

The brevity of information was a common thread throughout the specification of strategies. Proctor and colleagues’ definition of Action Target (specify targets) was operationalised as the unit of analysis and the level and characteristics of those targeted, with a separate code to identify the conceptual targets.

The level of participants was identified in each study, but the characteristics of participants lacked detail. We distinguished Proctor’s Action target (level and characteristics) from AACTT’s Actor element, as who the strategy targeted, rather than who completes the target behaviour or who was required to change. The characteristics of Proctor’s Action target, although not common in this review, can be distinct from the AACTT’s Actor. This was illustrated in Opondo and colleagues’ study where strategies were delivered at the hospital level, but individual health staff behaviours were required to change.

Aside from the description of the role of who was targeted (e.g. ‘physician’), only limited characteristics, such as age, gender and tenure were identified. Previous de-implementation literature found identifying who may have higher inappropriate prescribing rates and may be less likely to engage with de-implementation strategies, will help pinpoint and tailor strategies to where they are needed [37]. Healthcare professionals who were open to new evidence, younger and had less clinical experience tended to de-implement targeted practices more quickly [37]. Capturing key characteristics of those using the strategies helps gain insight into the circumstances that may contribute to de-implementation and where future efforts need to focus.

The Justification for the use of strategies was another area of insufficient reporting. The Justification is a valuable point of reporting to understand the reasons for choosing particular strategies and why they are suitable for the context and the behaviour [17]. Generally, empirical evidence and/or pragmatic rationales were offered as sources of justification. However, the reference to theory was sparse and mentioned by only two studies. The application of theory has previously been reported to be limited in strategy development literature [38, 39].

Theoretical justification offers insight into the mechanisms that influence behaviour change. Mechanisms are important in understanding how the strategy developers envisage the strategy changing the intended target behaviour [40]. Mechanisms could be inferred from the type of strategy being offered; however, the reports of strategy content varied. For example, strategies offering education can target various mechanisms such as knowledge or skills, to varying degrees. Multiple frameworks and theories are now available to facilitate de-implementation [8, 41, 42]. Better specification of theories or frameworks is key to understanding the mechanisms that have to change to facilitate de-implementation [17].

Following the analysis stage and research team discussions, an additional element to specify the participants ‘interactions’ with the strategy was identified as useful addition to these frameworks.

The specification of the ‘interactions’ participants must achieve to successfully engage with the strategy is needed. Even where the low-value behaviour and the content of the strategy were specified, there was little information about how healthcare professionals were expected to use the strategy. Strategies require health care professionals to perform behaviour outwith or in addition to their usual routine. Specifying these ‘interactions’ provided explicit details around these additional behaviours, to further understand how the strategy may fit in the workflow or if it is feasible or acceptable to the participants [43, 44].

Another area of reflection was the AACTT element of Context. The definition of Context is “The physical, emotional or social setting in which the Actor performs (or should/could perform) the Action” (p.5) [20]. This analysis found various levels and types of contexts reported, which meant we collected multiple layers of context including the capacity of setting, location, and the level of setting. Context is a complex and key area of health care improvement as strategy success can be context-dependent [45]. Many efforts have been taken to identify, measure and report all elements of context [46,47,48,49,50,51]. Extending the scope or amount of information captured in the Context element of the AACTT framework could help identify key influences in de-implementation such as Culture, which is often unconsidered [52].

This study utilised the AACTT and Proctor frameworks to specify low-value prescribing practices and related de-implementation strategies. Using two specification frameworks in conjunction allowed a comprehensive assessment of the quality of reporting of the behavioural targets and the strategy components used in de-implementation. These findings highlight the need for standardised reporting of strategies aimed at de-implementing healthcare professional prescribing behaviour. Implementation journal editors and research funders should consider requesting that de-implementation strategies be specified in accordance with the Proctor and AACTT frameworks, as per our suggestions. This would facilitate clear communication of the target behaviour and the strategies, which would allow for a better understanding of the de-implementation process and provide relevant high-quality information required for future replication.

Strengths and limitations

The strength of this review was its use of established specification frameworks. Utilising the Proctor and AACTT frameworks in conjunction allowed the specification of valuable information relevant to the de-implementation of unnecessary healthcare behaviours. These frameworks were functional and allowed the identification of additional elements that are relevant to de-implementation strategy reporting.

Limitations included the operationalisation of framework definitions and the nature of the analysis. Definitions were discussed with the research team and double coding ensured definitions were coherent and concise. It is possible that another research team could have operationalised elements differently. Additionally, t

he review, this analysis is based on, had a precise inclusion and exclusion criteria, which may have excluded more poorly reported studies, which could have led to an underestimation of suboptimal reporting.

Another limitation was the potential for overlap between the frameworks used. As definitions evolved, we attempted to keep within the definitions of the framework to gain as comprehensive and distinct information as possible. Although not identified often in this analysis, the distinction between the AACTT Actor and Proctor’s Action Target can be different populations. For example, in the case of patient-mediated strategies, attempts to change healthcare professional's (AACTT Actor) behaviour is influenced by targeting patients (Proctor, Action Target). Establishing distinct definitions provided guidance for our analysis.

Conclusions

In conclusion, this analysis provides a better understanding of how well the behavioural targets and the components of de-implementation strategies were reported. The use of AACTT and Proctor’s frameworks in conjunction offers a comprehensive way to specify and report de-implementation research and should be considered by authors, journal editors and funders. The ‘interactions’ of the participants using the strategy and the extension of the AACTT’s Context element were identified as additional considerations when reporting de-implementation strategies.

Abbreviations

AACTT:

Actor, Action Context, Target and Time Framework,

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Acknowledgements

Not applicable.

Funding

This review was part of a PhD studentship supported by the CRANES charity.

Author information

Authors and Affiliations

  1. Academic Urology Unit, University of Aberdeen, Aberdeen, U.K.

    Jennifer Dunsmore, Sara MacLennan, James N’Dow & Steven MacLennan

  2. Health Service Research Unit, University of Aberdeen, Aberdeen, U.K.

    Eilidh Duncan

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  2. Eilidh Duncan
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  3. Sara MacLennan
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Contributions

JD, ED, SJM and SM conceived and designed the study and agreed on the use of the AACTT and Proctor frameworks. JD coded 100% of the included studies. Definitions were agreed between JD, ED and SM. ED double-coded 45% of full texts to the frameworks. ED offered feedback on ongoing coding. JD drafted the manuscript and all authors contributed to its revisions.

Corresponding author

Correspondence to Jennifer Dunsmore.

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Not applicable.

Consent for publication

This study does not contain any identifiable information.

Availability of data and materials

The data analysed in this analysis is available from the corresponding author on reasonable request.

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The authors declare no competing interests.

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Supplementary Information

43058_2024_624_MOESM1_ESM.xlsx

Additional file 1. Identification of the behavioural elements as coded to the AACTT framework domians for control groups.

43058_2024_624_MOESM2_ESM.xlsx

Additional file 2. Identification of the intervention elements as coded to the Proctor framework domians for control groups.

43058_2024_624_MOESM3_ESM.xlsx

Additional file 3. Identification of the behavioural elements as coded to the AACTT framework domians for strategy arms.

43058_2024_624_MOESM4_ESM.xlsx

Additional file 4. Identification of the intervention elements as coded to the Proctor framework domians for strategy arms.

Additional file 5. Identification of the new 'Interactions' component for strategy and control arms.

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Dunsmore, J., Duncan, E., MacLennan, S. et al. Specifying behavioural and strategy components of de-implementation efforts targeting low-value prescribing practices in secondary health care. Implement Sci Commun 5, 88 (2024). https://doi.org/10.1186/s43058-024-00624-6

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Keywords

Implementation Science Communications

ISSN: 2662-2211