Rank Variation Research Articles

Managing transit-oriented development: A comparative analysis of expert groups and multi-criteria decision making methods

A key challenge for transport managers and planners in sustainable development is evaluating transit facilities' performance. While Multi-Criteria Decision-Making (MCDM) tools are often used, they can be influenced by experts' subjective biases. This study applies MCDM to assess Mass Rapid Transit (MRT) stations in Jakarta, Indonesia, focusing on Transit-Oriented Development (TOD). The primary goal is to compare stakeholder perspectives and MCDM methods, complemented by a sensitivity analysis and validation with real-world smart card data. The findings reveal significant differences in criteria weighting between Indonesian and non-Indonesian experts, and between academic and non-academic experts, especially in transit connectivity and land use diversity. The study also shows variations in station rankings across different MCDM methods. Sensitivity analysis identifies transit connectivity as the most critical criterion. Simple Additive Weighting (SAW) with linear normalisation aligns well with actual usage data and shows robustness in sensitivity analysis, making it the most reliable method for TOD evaluation. The study highlights the need for continuous TOD performance monitoring and the regular collection of real-world data on ridership and TOD indicators.

Consumer views on the healthiness of meat from various animal species: A comprehensive survey including fish

The study aimed to assess respondents’ opinions on healthiness of various types of meat derived from different animal species across multiple countries. A total of 1116 questionnaires from Spain, Italy, Poland, Hungary and China were analysed. According to the perception of all respondents, the ranking on the healthiness of different types of meat is as follows: fish (39.9%), rabbit (20.8%), chicken (15.1%), beef (13.4%), pork (4.9%), lamb (4.3%) and horse meat (1.6%). In specific countries, variations in rankings were observed. In Italy, Poland and Hungary fish claimed the top position, while in Spain rabbit meat was ranked the highest. Conversely, in China, fish and beef were considered the healthiest options. Gender exhibited only a marginal effect. Fish was considered the healthiest for all ages. With the exception of China, where beef overtook the second place, rabbit meat was in the second position. Regarding household income, a difference emerged only in the case of fish; the 'Live well' and 'Live very well' groups considered it healthier than those with lower incomes. The findings suggest that respondents’ opinions on the healthiness of different meat types could be leveraged for the development of marketing strategies with the meat industry.

The Effect of Mindfulness-Based SEL Programs on Reducing Stress and Improving Mental Health in Adolescents in Jordan

This study employed quantitative methodologies to investigate the impact of a 12-week mindfulness-based Social and Emotional Learning (SEL) program on lowering stress and improving mental well-being in a sample of 300 adolescents from Jordan. The study employed a stratified random sampling strategy to choose participants from diverse urban and rural populations. Data was collected utilizing the Perceived Stress Scale (PSS) and the Strengths and Difficulties Questionnaire (SDQ). The data evaluation involved the use of descriptive statistics, paired-sample t-tests, regression analysis, and an Analysis of Covariance (ANCOVA). The results indicate a significant decrease in stress levels and positive changes in mental health outcomes following the intervention. The regression analysis revealed that age was a likely predictor of changes in stress levels, indicating that older persons saw more significant declines. The analysis of covariance (ANCOVA) indicated significant gender-specific discrepancies in the impacts of the intervention. Independent samples t-tests revealed statistically significant variations in the average rankings across specific age groups and school environments. Proposed measures include implementing mindfulness-based social and emotional learning (SEL) programs in school systems, tailoring treatments to suit different demographic groups, examining gender-specific factors, and advocating for policy integration. Performing a longitudinal study is typically advised to assess the long-term sustainability of intervention results.

Research priorities for diagnosis, prognosis, and rehabilitation following concussion: results from a national survey of Australian health professionals

Purpose Recently, the Concussion James Lind Alliance Priority Setting Partnership (JLAPSP) (Canada) identified serious research gaps regarding diagnosis, management, and access to effective rehabilitation for concussion/mild traumatic brain injury (mTBI). Our aim was to determine if the same research priorities are important to Australian health professionals working in the concussion/mTBI field. Materials and methods A survey was distributed via professional networks, social media, professional group listservs, a research project noticeboard, and at conferences. It comprised of 25 of the highest ranked concussion research questions from the JLAPSP. We examined how professionals ranked the research questions and analyzed variation in ranking by clinical role and concussion/mTBI work experience. Results Our sample of 187 participants included medical and allied health professionals. Most participants were occupational therapists (22%), physiotherapists (18%), neuropsychologists (17%), and worked in Victoria (47%), New South Whales (18%), or Queensland (15%) in metropolitan areas. Health professionals ranked three research questions highest: identifying methods to predict prolonged recovery; effectiveness of early referral and treatment by a specialized concussion/mTBI team; and implementation studies on upskilling healthcare workers. Conclusions The research priorities identified can guide research efforts to improve the assessment, management, and rehabilitation of individuals with concussion/mTBI in Australia.

Public acceptability of carbon pricing: unravelling the impact of revenue recycling

ABSTRACT Carbon pricing has emerged as a prominent policy tool to mitigate climate change due to its proclaimed high efficiency and effectiveness. However, the successful and sustainable implementation of carbon pricing also depends on the public acceptability. This paper investigates how revenue recycling shapes public attitudes towards carbon pricing. A systematic literature review identified 48 relevant studies from 2004 to March 2023. Using qualitative content analysis I discern relevant factors and construct a typology of revenue recycling options. Subsequently, a consensus ordinal ranking of revenue recycling options, based on the empirical findings of the reviewed studies, synthesizes current knowledge. The results reveal that revenue recycling has a crucial impact on public acceptability. While environmental earmarking ranks highest, and reducing corporate taxes is the least preferred option, numerous other revenue recycling options lie between these two options. Moreover, the literature presents divergent findings regarding the level of public acceptability of specific revenue recycling options. The diversity of research contexts and designs introduces inherent limitations that may explain variations in rankings observed across different studies. Notably, I identify political trust related to and salience of revenue recycling as important factors affecting public acceptability of carbon pricing. Finally, this research offers a framework for policymakers seeking to design revenue recycling strategies that align with public preferences to implement carbon pricing.

A new class of composite indicators: The penalized power mean

In this paper we propose a new aggregation method for constructing composite indicators based on a penalization of the power mean. The idea underlying this approach consists in multiplying the power mean by a factor that accounts for the horizontal heterogeneity among indicators while penalizing units with a larger heterogeneity. In line with the minimum loss of information principle, the penalization factor proposed is proven to be linked to the loss of information generated when the indicators are substituted with their power means. As a consequence, the aggregation approach gives rise to the class of penalized power means and the penalized Benefit of the Doubt aggregative approach. Including heterogeneity makes the aggregation approach more suitable for refined rankings. Interestingly, the penalized power mean of order one coincides with the Mazziotta Pareto Index. Some theoretical properties of the penalized power means are proven, thus supporting the Mazziotta Pareto index. An empirical analysis of the Human Development Index in 2019 is presented. Comparisons of the rankings induced by the penalized and non-penalized Benefit of the Doubt and power mean aggregation approaches are shown. There are three main findings: the penalized power means satisfy the properties characterizing weakly monotone aggregation functions; the penalization reduces ranking variations while differentiating units with close means; and the geometric mean provides composite indicators whose ranking is closest to those obtained with power means of different order.

Using multi‐criteria decision‐making methods in prioritizing structural flood control solutions: A case study from Iran

AbstractEffective management of flood risks requires the prioritization of appropriate flood control solutions. This study aims to prioritize structural flood control options using multi‐criteria decision‐making (MCDM) methods. Four MCDM methods, namely analytic hierarchy process, technique for order preference by similarity to ideal solution, multi‐criteria optimization and compromise solution, and Fuzzy‐VIKOR are employed to assess and rank the flood control options based on multiple criteria. Field surveys, interviews with local authorities and experts, and on‐site assessments of existing flood control structures constituted the primary data collection methods. The findings demonstrate the effectiveness of reservoir dams, retention basins, and levees as viable solutions. Conversely, flood control gates and the no‐project options were assigned lower priorities. The findings highlight the importance of considering multiple MCDM methods to account for variations in rankings. The study provides valuable insights into the decision‐making process for prioritizing flood control options in the study area. These findings can assist policymakers and stakeholders in effectively allocating resources and implementing appropriate structural flood control measures to mitigate flood risks.

Using Generative AI to Improve the Performance and Interpretability of Rule-Based Diagnosis of Type 2 Diabetes Mellitus

Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy.

Graphical Association Analysis for Identifying Variation in Provider Claims for Joint Replacement Surgery.

Identifying potentially fraudulent or wasteful medical insurance claims can be difficult due to the large amounts of data and human effort involved. We applied unsupervised machine learning to construct interpretable models which rank variations in medical provider claiming behaviour in the domain of unilateral joint replacement surgery, using data from the Australian Medicare Benefits Schedule. For each of three surgical procedures reference models of claims for each procedure were constructed and compared analytically to models of individual provider claims. Providers were ranked using a score based on fees for typical claims made in addition to those in the reference model. Evaluation of the results indicated that the top-ranked providers were likely to be unusual in their claiming patterns, with typical claims from outlying providers adding up to 192% to the cost of a procedure. The method is efficient, generalizable to other procedures and, being interpretable, integrates well into existing workflows.

Benchmarking Perception to Streaming Inputs in Vision-Centric Autonomous Driving

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. Although a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming input characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming input characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming input features, we introduce a backbone switcher based on the identified streaming input characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming input features.

Robust Online Tensor Completion for IoT Streaming Data Recovery.

Reliable data measurement is considered to be one of the critical ingredients for variant Internet of Things (IoT) applications. Gaining full knowledge of measurement data is becoming increasingly crucial to ensure a satisfactory user experience. However, data missing and corruption are inevitable in practical applications, which motivates us to study how to accurately recover the missing IoT measurement data in the presence of outliers. The data recovery problem can be formulated as a tensor completion (TC) problem. Existing TC methods are built on the assumption that the rank of the underlying tensor is fixed, which is not suitable for long data sequences in practice. Consequently, based on the characteristics of IoT streaming data, we assume that the data tensor lies in time-varying subspace, and an accurate estimate of the rank is a prerequisite for filling the missing entries and achieving robustness of the variations in both rank and noise. We built up an updatable framework based on dynamic CANDECOMP/PARAFAC (CP) decomposition. In addition, an efficient algorithm, called temporal multi-aspect streaming (T-MUST), is introduced to solve the optimization problem that originates in our developed model. It is worth noting that the proposed algorithm allows time-varying tensor rank and enables the rank changes could be detected and tracked automatically. Theoretical analysis indicates that T-MUST enjoys a geometric convergence rate. Numerical experiments conducted on various synthetic and real-world datasets empirically validate the superiority of the proposed T-MUST in both efficiency and effectiveness.

Sustainable healthy diet modeling for a plant-based dietary transitioning in the United States.

The potential environmental and nutritional benefits of plant-based dietary shifts require thorough investigation to outline suitable routes to achieve these benefits. Whereas dietary consumption is usually in composite forms, sustainable healthy diet assessments have not adequately addressed composite diets. In this study, we build on available data in the Food4HealthyLife calculator to develop 3 dietary concepts (M) containing 24 model composite diet scenarios (S) assessed for their environmental and nutritional performances. The Health Nutritional Index (HENI) and Food Compass scoring systems were used for nutritional quality profiling and estimates of environmental impact were derived from previously reported midpoint impact values for foods listed in the What We Eat in America database. The diets were ranked using the Kruskal‒Wallis nonparametric test, and a dual-scale data chart was employed for a trade-off analysis to identify the optimal composite diet scenario. The results showcased a distinct variation in ranks for each scenario on the environment and nutrition scales, describing an inherent nonlinear relationship between environmental and nutritional performances. However, trade-off analysis revealed a diet with 10% legumes, 0.11% red meat, 0.28% processed meat and 2.81% white meat could reduce global warming by 54.72% while yielding a diet quality of 74.13 on the Food Compass Scoring system. These observations provide an interesting forecast of the benefits of transitioning to an optimal plant- and animal-based dieting pattern, which advances global nutritional needs and environmental stewardship among consumers.

Evaluation of IPCC Models’ Performance in Simulating Late-Twentieth-Century Weather Patterns and Extreme Precipitation in Southeastern China

Climate model evaluation work has made progress both in theory and practice, providing strong support for better understanding and predicting climate change. However, at the weather scale, there is relatively little assessment of climate models in terms of daily-scale climate phenomena, such as storm frequency and intensity. These weather-scale variables are of significant importance for our understanding of the impacts of climate change. In order to assess the capability of climate models to simulate weather-scale climate patterns, this study employs Self-Organizing Maps (SOMs) for weather pattern classification. By combining different evaluation metrics, varying the number of SOM types, changing the size of the study area, and altering the reference datasets, the climate models are evaluated to ensure the robustness of the assessment results. The results demonstrate that the size of the study area is positively correlated with observed differences, and there are correlations among different evaluation metrics. The highest correlation is observed between evaluation metrics in large-scale and small-scale spatial domains, while the correlation with SOM size is relatively low. This suggests that the choice of evaluation metrics has a minor impact on model ranking. Furthermore, when comparing the correlation coefficients calculated using the same evaluation metrics for different-sized regions, a significant positive correlation is observed. This indicates that variations in the size of the study area do not significantly affect model ranking. Further investigation of the relationship between model performance and different SOM sizes shows a significant positive correlation. The impact of dataset selection on model ranking is also compared, revealing high consistency. This enhances the reliability of model ranking. Taking into account the influence of evaluation metric selection, SOM size, and reanalysis data selection on model performance assessment, significant variations in model ranking are observed. Based on cumulative ranking, the top five models identified are ACCESS1-0, GISS-E2-R, GFDL-CM3, MIROC4h, and GFDL-ESM2M. In conclusion, factors such as evaluation metric selection, study area size, and SOM size should be considered when assessing model ranking. Weather pattern classification plays a crucial role in climate model evaluation, as it helps us better understand model performance in different weather systems, assess their ability to simulate extreme weather events, and improve the design and evaluation methods of model ensemble predictions. These findings are of great significance for optimizing and strengthening climate model evaluation methods and provide valuable insights for future research.

Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers.

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015-2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models (random forest and neural network), we identified the relative importance and variation in ranking of predictors (historical WNND cases, climate anomalies, human demographics, and land use) across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non-significant differences in performance relative to the baseline model (negative binomial model fit per hexagon). No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines.

Top 10 marine litter items on the seafloor in European seas from 2012 to 2020

We studied the ten most frequently encountered litter items from the seafloor in European seas to advance actions and inform future mitigation measures to reduce marine litter and the associated social, economic and environmental impacts it has on European seas and beyond. Data were collected during trawl surveys from 2012 to 2020 as part of national and regional marine litter monitoring programmes in the Greater North Sea (5652 trawls), Celtic Seas (3505), Bay of Biscay (651), and Baltic Sea (3688). A Bayesian approach is used to quantify the variation in the item rankings. Overall, plastic items predominate in the top positions in each area. Synthetic rope, plastic sheets, monofilament fishing line and plastic bags occupy four of the top five positions for each of the Greater North Sea, Celtic Seas and the Bay of Biscay. Items from fishing and rope (representing mainly other maritime activities) are strongly represented in the top ten lists from three of our four areas, with synthetic rope, fishing nets, and tangled and untangled monofilament fishing line listed in the top seven positions for the Greater North Sea, Celtic Seas and the Bay of Biscay. The top ten items in the Baltic Sea are of a different profile to the other regions, but the most commonly caught items are still predominantly plastic, with plastic sheets, other plastic items and plastic bags occupying three of the top four positions.The findings in this study highlight the need to address sea-based sources to try and eliminate litter from fishing and maritime activities. Measures such as improved port reception facilities, marking of fishing gear, promoting reporting of the loss of fishing gear and increasing public awareness should be introduced.

Low Rank and Total Variation Based Two-Phase Method for Image Deblurring with Salt-and-Pepper Impulse Noise

Low Rank and Total Variation Based Two-Phase Method for Image Deblurring with Salt-and-Pepper Impulse Noise

Free-Breathing Liver Fat, R₂* and B₀ Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction.

This work introduced a stack-of-radial multi-echo asymmetric-echo MRI sequence for free-breathing liver volumetric acquisition. Regularized model-based reconstruction was implemented in Berkeley Advanced Reconstruction Toolbox (BART) to jointly estimate all physical parameter maps (water, fat, R2∗ , and B0 field inhomogeneity maps) and coil sensitivity maps from self-gated k -space data. Specifically, locally low rank and temporal total variation regularization were employed directly on physical parameter maps. The proposed free-breathing radial technique was tested on a water/fat & iron phantom, a young volunteer, and obesity/diabetes/hepatic steatosis patients. Quantitative fat fraction and R2∗ accuracy were confirmed by comparing our technique with the reference breath-hold Cartesian scan. The multi-echo radial sampling sequence achieves fast k -space coverage and is robust to motion. Moreover, the proposed motion-resolved model-based reconstruction allows for free-breathing liver fat and R2∗ quantification in multiple motion states. Overall, our proposed technique offers a convenient tool for non-invasive liver assessment with no breath holding requirement.

Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar.

While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.

Smartphone Addiction Assessment Using Pythagorean Fuzzy CRITIC-TOPSIS

Addiction to smartphones, particularly among adolescents, has reached alarming proportions, rivaling or perhaps exceeding internet addiction as the most widespread kind of dependence in modern culture. Evaluating the degree of problematic smartphone use habits by experts and identifying the vulnerable ones to steer to the right treatment program has become a critical issue. Since such a task may involve an abundance of criteria and candidates, as well as the inherent subjectivity of multiple decision experts participating in the process, the assessment of smartphone addiction can be framed as an uncertain multi-criteria decision-making (MCDM) problem. As an extension of intuitionistic fuzzy sets, Pythagorean fuzzy sets can be used to efficiently manage ambiguity and uncertainty during decision-making. This study provides an integrated fuzzy MCDM methodology based on Pythagorean fuzzy sets for evaluating the smartphone addiction level of adolescents. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to determine the importance levels of criteria in an objective manner, and smartphone addiction levels of the selected candidates are ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A sensitivity analysis is conducted to examine the variations in candidate rankings caused by changes to the criteria and weights of the decision experts. Moreover, in the context of comparative analysis, the Evaluation based on Distance from Average Solution (EDAS) approach is used to validate the acquired findings.

Factors Influencing Community Participation in Internet Interventions Compared With Research Trials: Observational Study in a Nationally Representative Adult Cohort.

Digital mental health (DMH) programs can be effective in treating and preventing mental health problems. However, community engagement with these programs can be poor. Understanding the barriers and enablers of DMH program use may assist in identifying ways to increase the uptake of these programs, which have the potential to provide broad-scale prevention and treatment in the community. In this study, we aimed to identify and compare factors that may influence participation in DMH programs in practice and research trials, identify any respondent characteristics that are associated with these factors, and assess the relationship between intentions to use DMH programs and actual uptake. Australian adults aged ≥18 years were recruited from market research panels to participate in the study. The sample was representative of the Australian adult population based on age, gender, and location. Participants completed a cross-sectional web-based survey assessing demographic characteristics, mental health symptom measures, attitudes and use of DMH programs in practice and in research studies, and the factors influencing their use in both settings. Across both research and practice, trust in the organization delivering the service or trial was the top-ranked factor influencing participation, followed by anonymity or privacy and adequate information. There was little variation in rankings across demographic groups, including intentions to use DMH programs or mental health status. Intentions to use DMH programs were a strong predictor of both current (odds ratio 2.50, 99% CI 1.41-4.43; P<.001) and past (odds ratio 2.98, 99% CI 1.71-5.19; P<.001) use behaviors. Efforts to increase the uptake of DMH programs or participation in research trials should focus on clearly communicating the following to users: the legitimacy of the organization delivering the program, security and use of participant data, and effectiveness of DMH programs.