Evaluation Review最新文献

Sustainable agriculture is crucial for achieving SDG2 and building a resilient climate-food system. This study provides a nuanced understanding of factors that influence the adoption of precision agriculture technology by Vietnamese smallholder rice farmers as a sustainable agricultural solution. The study's objectives are: (1) to provide a nuanced understanding of factors that influence adoption of precision agriculture technology by Vietnamese smallholder rice farmers; and (2) to identify factors associated with agricultural practice in Vietnam that may impact the adoption of precision agriculture technology. The study conducted 35 semi-structured interviews with smallholder rice farmers and agriculture experts. Data were analyzed using iterative thematic analysis. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used for empirical analysis. The UTAUT constructs of Performance expectancy, Effort expectancy, Government support, and Social influence were found to be determinants of adoption. Trialability and Observability impact Effort expectancy and Performance expectancy. We also discovered that the performance of agricultural cooperatives and support of lead firms play a crucial role in facilitating agricultural technology adoption by Vietnamese smallholder rice farmers. The results confirm the applicability of UTAUT in defining smallholders' behavioural intention and predicting agricultural technology adoption. The study also provides constructive feedback on the UTAUT model by highlighting the effect of agricultural cooperatives' performance as innovation intermediaries and of the support of lead firms.

The COVID-19 pandemic has exacerbated the local debt in Spanish municipalities with negative consequences on the macroeconomic financial stability at national and eurozone level. The main objective is to identify the causes of public debt per capita in four groups of Spanish municipalities according to size. It is based on a quantitative analysis based on correlational and causal-comparative approaches. It consists in the construction of panel quantile regressions (MMQ) and mean group (ME) estimators to explain public debt per capita in Spanish municipalities. Moreover, the Juodis test for causality analysis in panel data is applied. The research is constructed around various types of potential determinants related to economic factors (GDP per capita and unemployment rate), demographic factors (population under 15 and population over 65), and political factors (political party, ideology, and political strength). The results based on MMQR for the period 2011-2020 indicate common factors that reduce local debt (short-run economic growth), but also differences between clusters in what concerns factors that increase or decrease the debt. The Juodis et al. (2021) test shows that growth and unemployment are factors that influence the level of public debt in all groups of municipalities except one (5001-20,000 inhabitants) where political party affects debt. These empirical findings support policy proposals to reduce the local debt in Spanish municipalities. The main initiatives to reduce debt should be based on the promotion of economic growth and creation of new jobs, especially for young people.

This paper describes a process evaluation of a 'wise' intervention that took place in six acute care units in two medical-surgical teaching hospitals in the United States during 2016-2017. 'Wise' interventions are short, inexpensive interventions that depend on triggering specific psychological mechanisms to achieve behaviour change. This study sought to increase the hand hygiene compliance (HHC) rates before entering a patient's room among nurses. The intervention centred on the use of threat to professional identity to prompt improved HHC. Through questionnaires administered to intervention participants and the implementation facilitator, together with independent observation of intervention delivery, we examined whether the steps in the Theory of Change occurred as expected. We found that aspects of the implementation-including mode of delivery, use of incentives, and how nurses were recruited and complied with the intervention-affected reach and likely effectiveness. While components of the intervention's mechanisms of impact-such as the element of surprise-were successful, they ultimately did not translate into performance of the target behaviour. Performance was also not affected by use of an implementation intention as repeated performance of HHC over years of being a nurse has likely already established well-ingrained practices. Context did have an effect; the safety culture of the units, the involvement of the Nurse Managers, the level of accountability for HHC in each unit, and the hospitals themselves all influenced levels of engagement. These conclusions should have implications for those interested in the applicability of 'wise' interventions and those seeking to improve HHC in hospitals.

Background: While randomized controlled trials (RCTs) are typically considered the gold standard of program evaluation, they are infrequently chosen by public sector leaders, defined as government and nonprofit decision-makers, when an impact evaluation is required. Objectives: This study provides descriptive evidence on RCT aversion among public sector leaders and attempts to understand what factors affect their likelihood of choosing RCTs for impact evaluations. Research Design: The authors ask if public sector leaders follow similar preference patterns found among non-public sector leaders when choosing either an RCT or a quasi-experimental design and use a survey experiment to determine which factors affect the RCT choice. Subjects: The study sample includes 2050 public sector leaders and a comparison group of 2060 respondents who do not lead public sector organizations. Measures: The primary outcome measure is selecting an RCT as the preferred evaluation option. Results: When asked to make a decision about an impact evaluation, the majority of people do not choose an RCT. While also averse to RCTs, public sector leaders are about 13% more likely to prefer a RCT to a quasi-experimental evaluation compared to the general population. Public sector leaders are less likely to use RCTs for evaluations of more intense interventions, potentially because they are perceived to be superior to the options available for the control group. Conclusion: Funders should be aware that when given a choice, public sector leaders prefer other options to RCTs. Greater awareness of the benefits of RCTs could increase their use in the public sector.

Recent attention to the causal identification of spending impacts provides improved estimates of spending outcomes in a variety of circumstances, but the estimates are substantially different across studies. Half of the variation in estimated funding impact on test scores and over three-quarters of the variation of impacts on school attainment reflect differences in the true parameters across study contexts. Unfortunately, inability to describe the circumstances underlying effective school spending impedes any attempts to generalize from the extant results to new policy situations. The evidence indicates that how funds are used is crucial to the outcomes, but such factors as targeting of funds or court interventions fail to explain the existing pattern of results.

Systematic reviews and meta-analyses are viewed as potent tools for generalized causal inference. These reviews are routinely used to inform decision makers about expected effects of interventions. However, the logic of generalization from research reviews to diverse policy and practice contexts is not well developed. Building on sampling theory, concerns about epistemic uncertainty, and principles of generalized causal inference, this article presents a pragmatic approach to generalizability assessment for use with systematic reviews and meta-analyses. This approach is applied to two systematic reviews and meta-analyses of effects of "evidence-based" psychosocial interventions for youth and families. Evaluations included in systematic reviews are not necessarily representative of populations and treatments of interest. Generalizability of results is limited by high risks of bias, uncertain estimates, and insufficient descriptive data from impact evaluations. Systematic reviews and meta-analyses can be used to test generalizability claims, explore heterogeneity, and identify potential moderators of effects. These reviews can also produce pooled estimates that are not representative of any larger sets of studies, programs, or people. Further work is needed to improve the conduct and reporting of impact evaluations and systematic reviews, and to develop practical approaches to generalizability assessment and guide applications of interventions in diverse policy and practice contexts.

This chapter begins with an overview of recent developments that have encouraged and facilitated greater use of research syntheses, including Meta-Analysis, to guide public policy and practice in education, workforce development, and social services. It discusses the role of Meta-Analysis for improving knowledge of the effectiveness of programs, policies, and practices and the applicability and generalizability of that knowledge to conditions other than those represented by the study samples and settings. The chapter concludes with recommendations for improving the potential of Meta-Analysis to accelerate knowledge development through changing how we design, conduct, and report findings of individual studies to maximize their usefulness in Meta-Analysis as well as how we produce and report Meta-Analysis findings. The paper includes references to resources supporting the recommendations.

Assessing the transferability of lessons from social research or evaluation continues to raise challenges. Efforts to identify transferable lessons can be based on two different forms of argumentation. The first draws upon statistics and causal inferences. The second involves constructing a reasoned case based on weighing up different data collected along the causal chain from designing to delivery. Both approaches benefit from designing research based upon existing evidence and ensuring that the descriptions of the programme, context, and intended beneficiaries are sufficiently rich. Identifying transferable lessons should not be thought of as a one-off event but involves contributing to the iterative and learning of a scientific community. To understand the circumstances under which findings can be confidently transferred, we need to understand: (1) How far and why outcomes of interest have multiple, interacting and fluctuating causes. (2) The program design and implementation capacity. (3) Prior knowledge and causal landscapes (and how far these are included in the theory of change). (4) New and relevant knowledge; what can we learn in our 'disputatious community of truth seekers'.

This paper describes how mixed methods can improve the value and policy relevance of impact evaluations, paying particular attention to how mixed methods can be used to address external validity and generalization issues. We briefly review the literature on the rationales for using mixed methods; provide documentation of the extent to which mixed methods have been used in impact evaluations in recent years; describe how we developed a list of recent impact evaluations using mixed methods and the process used to conduct full-text reviews of these articles; summarize the findings from our analysis of the articles; discuss three exemplars of using mixed methods in impact evaluations; and discuss how mixed methods have been used for studying and improving external validity and potential improvements that could be made in this area. We find that mixed methods are rarely used in impact evaluations, and we believe that increased use of mixed methods would be useful because they can reinforce findings from the quantitative analysis (triangulation), and they can also help us understand the mechanism by which programs have their impacts and the reasons why programs fail.