Evaluating a complex health promotion intervention: case application of three systems methods

Rosas, Scott R.; Knight, Erin

doi:10.1080/09581596.2018.1455966

Cited by 17 publications

(23 citation statements)

References 33 publications

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“… 43 Additionally, the use of SNA programmes to analyse data was reported as a strength in terms of the ease of use but as a limitation in terms of the need for special training and experience. 19 27 44 The visualisation of SNA results can strengthen the interpretation of the results. 44 However, a reported limitation was that complexity cannot be captured in simplified visuals.…”

Section: Resultsmentioning

confidence: 84%

“… 31 Reported limitations related to data collection were possible recall bias due to self-reported data, the challenge of obtaining responses, and non-respondent data. 19 28–30 37 43–46 The absence of nonrespondent data may introduce potential bias and can therefore dramatically affect network representation. 19 Reported strengths were that SNA data are easy to collect 28 and that data can be collected by various methods, 19 including specific SNA tools (NET map, Social Network Diagnostic Tool and Partner Tool).…”

Section: Resultsmentioning

confidence: 99%

“… 19 28–30 37 43–46 The absence of nonrespondent data may introduce potential bias and can therefore dramatically affect network representation. 19 Reported strengths were that SNA data are easy to collect 28 and that data can be collected by various methods, 19 including specific SNA tools (NET map, Social Network Diagnostic Tool and Partner Tool). 31 40 47 Regarding analysis, the quantitative results that SNA yields can be combined with other statistical approaches.…”

Section: Resultsmentioning

confidence: 99%

“… 44 However, a reported limitation was that complexity cannot be captured in simplified visuals. 19 Additionally, the interpretation of the results was reported as strength, as SNA provides insights into the relationships, positions, structure and strength of a network. 19 31 48 However, the generalisability of SNA results is limited due to the unique nature of a network.…”

Section: Resultsmentioning

confidence: 99%

“…Fifteen studies reported implications of using SNA in developing and evaluating complex interventions. 19 27 28 30 31 33 35 36 43–46 48–51 Three studies reported the wider use of SNA in their topic of research, namely, interprofessional education, train-the-trainer programmes and the evaluation of nursing interventions. 27 45 46 Figure 2 shows a graphical framework that summarises reported strengths of the application of SNA and reported implications, and connects their content to the dimensions of complexity.…”

Section: Resultsmentioning

confidence: 99%

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Value of social network analysis for developing and evaluating complex healthcare interventions: a scoping review

et al. 2020

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ObjectivesMost complex healthcare interventions target a network of healthcare professionals. Social network analysis (SNA) is a powerful technique to study how social relationships within a network are established and evolve. We identified in which phases of complex healthcare intervention research SNA is used and the value of SNA for developing and evaluating complex healthcare interventions.MethodsA scoping review was conducted using the Arksey and O’Malley methodological framework. We included complex healthcare intervention studies using SNA to identify the study characteristics, level of complexity of the healthcare interventions, reported strengths and limitations, and reported implications of SNA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews 2018 was used to guide the reporting.ResultsAmong 2466 identified studies, 40 studies were selected for analysis. At first, the results showed that SNA seems underused in evaluating complex intervention research. Second, SNA was not used in the development phase of the included studies. Third, the reported implications in the evaluation and implementation phase reflect the value of SNA in addressing the implementation and population complexity. Fourth, pathway complexity and contextual complexity of the included interventions were unclear or unable to access. Fifth, the use of a mixed methods approach was reported as a strength, as the combination and integration of a quantitative and qualitative method clearly establishes the results.ConclusionSNA is a widely applicable method that can be used in different phases of complex intervention research. SNA can be of value to disentangle and address the level of complexity of complex healthcare interventions. Furthermore, the routine use of SNA within a mixed method approach could yield actionable insights that would be useful in the transactional context of complex interventions.

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Section: Resultsmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Value of social network analysis for developing and evaluating complex healthcare interventions: a scoping review

et al. 2020

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Introducing systems‐ and complexity‐informed evaluation

Gates

Walton

Vidueira

et al. 2021

New Drctns Evaluation

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For the last several decades and recently amidst the COVID‐19 pandemic, many in the global evaluation communities call for shifts from linear, reductionist ways of thinking and working to approaches that embrace systems and complexity. In this introductory chapter, we orient readers to key systems and complexity traditions and terms and how these have been put to use in the evaluation field. Doing so provides a foundation from which to engage with the subsequent chapters. We close this chapter with highlights from the case examples featured in this issue.

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Prioritizing decision‐making of health and well‐being response tactics: Incorporating organizational and individual shared demands

Zhou

Sun

et al. 2023

Stress and Health

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As a major energy source in China, the occupational health and well‐being (OHW) of miners is a priority. Various statistical techniques have been used to identify factors or assess OHW to provide valuable information for the implementation of health promotion activities. The main bottleneck is the limited focus on solutions that address the demands of both organizations and individuals, and scientific and effective decision‐making is pending. Therefore, this study describes the OHW mechanism covering both antecedents and consequences through the driving force‐pressure‐state‐impact‐response model. A probabilistic model of management tradeoff analysis was established by using a Bayesian decision network. Causal relationships and dependencies between multiple factors are captured visually. The model was verified and applied with samples of miners (N = 816). The results showed that the comprehensive strategy (R5) was the best tactic, and the management effect of stress (R2) and vulnerability (R3) was prominent. This study provides a valuable tool for managers to identify priority management factors. Prioritizing tactics formulated from dual demands of organizational and individual can ensure project feasibility, operability, and effectiveness. This study is a novel attempt to combine theory with practice, which is timely and necessary for management.

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Evaluating a complex health promotion intervention: case application of three systems methods

Cited by 17 publications

References 33 publications

Value of social network analysis for developing and evaluating complex healthcare interventions: a scoping review

Value of social network analysis for developing and evaluating complex healthcare interventions: a scoping review

Introducing systems‐ and complexity‐informed evaluation

Prioritizing decision‐making of health and well‐being response tactics: Incorporating organizational and individual shared demands

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