DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath.

Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.

Does subnetting and port hardening influence human adversarial decisions? An investigation via a HackIT tool.

Prior research in cyber deception has investigated the effectiveness of the timing of deception on human decisions using simulation tools. However, there exists a gap in the literature on how the availability of subnets and port-hardening influence human decisions to attack a system. We tested the influence of subnets and port-hardening on human attack decisions in a simulated environment using the HackIT tool. Availability of subnets (present/absent) within a network and port-hardening (easy-to-attack/hard-to-attack) were varied across four between-subject conditions (N = 30 in each condition): with-subnet with easy-to-attack, with-subnet with hard-to-attack, without-subnet with easy-to-attack, and without-subnet with hard-to-attack. In with-subnet conditions, 40 systems were connected in a hybrid topology network with ten subnets connected linearly, and each subnet contained four connected systems. In without-subnet conditions, all 40 systems were connected in a bus topology. In hard-to-attack (easy-to-attack) conditions, the probabilities of successfully attacking real systems and honeypots were kept low (high) and high (low), respectively. In an experiment, human participants were randomly assigned to one of the four conditions to attack as many real systems as possible and steal credit card information. Results revealed a significant decrease in the proportion of real system attacks in the availability of subnetting and port hardening within the network. Also, more honeypots were attacked in with-subnet conditions than without-subnet conditions. Moreover, a significantly lower proportion of real systems were attacked in the port-hardened condition. This research highlights the implications of subnetting and port-hardening with honeypots to reduce real system attacks. These findings are relevant in developing advanced intrusion detection systems trained on hackers' behavior.

Does correlation heuristic dependence reduce due to classroom teaching? A case study from India.

Introduction: People worldwide have problems understanding the basic stock-flow principles (e.g., correlation heuristic), which govern many everyday tasks. Perhaps, teaching system dynamic concepts in classroom settings might reduce people's dependence on the correlation heuristic. However, limited literature exists on the effectiveness of classroom curricula in reducing reliance on the correlation heuristic. The present research aims to bridge this gap and empirically understand the effects of classroom teaching programs on reducing people's reliance on correlation heuristic and improving people's ability to understand stock-flow concepts. By taking a case from a reputed technology Institute in India, the present research examines how classroom teaching of system dynamics concepts might help students reduce their dependence on the correlation heuristic. Methods: The experiment consisted of two between-subjects conditions: the experimental and the control (N = 45 in each condition). The experimental condition consisted of randomly registered students that were taught system dynamics principles over 5-months of classroom training. Though, no teaching took place in the control condition. Participants in both conditions were evaluated on their ability to solve stock-flow problems. Results: Participants in the experimental condition were found to perform better in solving stock-flow problems than subjects in the control condition, and they also relied less on the correlation heuristic. Discussion: We emphasize the relevance of system dynamics education in graduate curricula in alleviating reliance on the correlation heuristic.

Learning from feedback: Evaluation of dynamic decision-making in virtual reality under various repetitive training frameworks.

Dynamic decision-making involves a series of interconnected interdependent confluence of decisions to be made. Experiential training is preferred over traditional methods to train individuals in dynamic decision-making. Imparting experiential training in physical settings can be very expensive and unreliable. In virtual reality (VR), synthetic environments play a significant role in providing flexible and cost-effective training environments to enhance dynamic decision-making. However, it is still unclear how VR can be used to impart dynamic decision-making training to increase cognitive performance in complex situations. Besides, different repetitive training methods like desirable difficulty framework and heterogeneity of practice have been evaluated on generic cognitive and motor tasks. However, an evaluation of how these repetitive training methods facilitate dynamic decision-making in an individual in a virtual complex environment setting is lacking in the literature. The objective of this study is to evaluate the effect of different repetitive training methods in immersive VR on dynamic decision-making in a complex search-and-shoot environment. In a lab-based experiment, 66 healthy subjects are divided equally and randomly into three between-subject training conditions: heterogenous, difficult, and sham. On Day 1, all the participants, regardless of the condition, executed an environment of a baseline difficulty level. From Days 2 to 7, the participants alternatively executed the novice difficulty and expert difficulty versions of the environment in the heterogenous condition. In difficult conditions, the participants executed the expert difficulty version of the environment from Days 2 to 7. In the sham condition, the participants executed an unrelated VR environment from Days 2 to 7. On Day 8, the participants executed the baseline difficulty version of the environment again in all the conditions. Various performance and workload-based measures were acquired. Results revealed that the participants in the heterogenous and difficult conditions performed significantly better on Day 8 compared with Day 1. The results inferred that a combination of immersive VR environment with repetitive heterogenous training maximized performance and decreased cognitive workload at transfer. We expect to use these conclusions to create effective training environments in VR for imparting training to military personnel in dynamic decision-making scenarios.

Life and Death Decisions and COVID-19: Investigating and Modeling the Effect of Framing, Experience, and Context on Preference Reversals in the Asian Disease Problem.

Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between-subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk-seeking behavior among participants. We developed cognitive and heuristics models using instance-based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience-based cognitive models could help build artificial intelligence models with fewer preference reversals.

Learning About the Effects of Alert Uncertainty in Attack and Defend Decisions via Cognitive Modeling.

OBJECTIVE: We aim to learn about the cognitive mechanisms governing the decisions of attackers and defenders in cybersecurity involving intrusion detection systems (IDSs). BACKGROUND: Prior research has experimentally studied the role of the presence and accuracy of IDS alerts on attacker's and defender's decisions using a game-theoretic approach. However, little is known about the cognitive mechanisms that govern these decisions. METHOD: To investigate the cognitive mechanisms governing the attacker's and defender's decisions in the presence of IDSs of different accuracies, instance-based learning (IBL) models were developed. One model (NIDS) disregarded the IDS alerts and one model (IDS) considered them in the instance structure. Both the IDS and NIDS models were trained in an existing dataset where IDSs were either absent or present and they possessed different accuracies. The calibrated IDS model was tested in a newly collected test dataset where IDSs were present 50% of the time and they possessed different accuracies. RESULTS: Both the IDS and NIDS models were able to account for human decisions in the training dataset, where IDS was absent or present and it possessed different accuracies. However, the IDS model could accurately predict the decision-making in only one of the several IDS accuracy conditions in the test dataset. CONCLUSIONS: Cognitive models like IBL may provide some insights regarding the cognitive mechanisms governing the decisions of attackers and defenders in conditions not involving IDSs or IDSs of different accuracies. APPLICATION: IBL models may be helpful for penetration testing exercises in scenarios involving IDSs of different accuracies.

Experience in a Climate Simulator: Influence of Probability Function and Feedback on Decisions Against Climate Change.

Research indicates that people continue to exhibit "wait-and-see" preferences toward climate change, despite constant attempts to raise awareness about its cataclysmic effects. Experiencing climatic catastrophes via simulation tools has been found to affect the perception of people regarding climate change and promote pro-environmental behaviors. However, not much is known about how experiential feedback and the probability of climate change in a simulation influence the decisions of people. We developed a web-based tool called Interactive Climate Change Simulator (ICCS) to study the impact of different probabilities of climate change and the availability of feedback on the monetary actions (adaptation or mitigation) taken by individuals. A total of 160 participants from India voluntarily played ICCS across four between-subject conditions (N = 40 in each condition). The conditions differed based on the probability of climate change (low or high) and availability of feedback (absent or present). Participants made mitigation and adaptation decisions in ICCS over multiple years and faced monetary consequences of their decisions. There was a significant increase in mitigation actions against climate change when the feedback was present compared to when it was absent. The mitigation and adaptation investments against climate change were not significantly affected by the probability of climate change. The interaction between probability of climate consequences and availability of feedback was significant: In the presence of feedback, the high probability of climate change resulted in higher mitigation and adaptation investments against climate change. Overall, the experience gained in the ICCS tool helped alleviate peoples' "wait-and-see" preferences and increased the monetary investments to counter climate change. Simulation tools like ICCS have the potential to increase people's understanding of climatic disasters and can act as a useful aid for educationalists and policymakers.

Influence of Network Size on Adversarial Decisions in a Deception Game Involving Honeypots.

Deception via honeypots, computers that pretend to be real, may provide effective ways of countering cyberattacks in computer networks. Although prior research has investigated the effectiveness of timing and amount of deception via deception-based games, it is unclear as to how the size of the network (i.e., the number of computer systems in the network) influences adversarial decisions. In this research, using a deception game (DG), we evaluate the influence of network size on adversary's cyberattack decisions. The DG has two sequential stages, probe and attack, and it is defined as DG (n,k, γ), where n is the number of servers, k is the number of honeypots, and γ is the number of probes that the adversary makes before attacking the network. In the probe stage, participants may probe a few web servers or may not probe the network. In the attack stage, participants may attack any one of the web servers or decide not to attack the network. In a laboratory experiment, participants were randomly assigned to a repeated DG across three different between-subject conditions: small (20 participants), medium (20 participants), and large (20 participants). The small, medium, and large conditions used DG (2, 1, 1), DG (6, 3, 3), and DG (12, 6, 6) games, respectively (thus, the proportion of honeypots was kept constant at 50% in all three conditions). Results revealed that in the small network, the proportions of honeypot and no-attack actions were 0.20 and 0.52, whereas in the medium (large) network, the proportions of honeypot and no-attack actions were 0.50 (0.50) and 0.06 (0.03), respectively. There was also an effect of probing actions on attack actions across all three network sizes. We highlight the implications of our results for networks of different sizes involving deception via honeypots.

Computational cognitive modeling and validation of Dp140 induced alteration of working memory in Duchenne Muscular Dystrophy.

Duchenne Muscular Dystrophy has emerged as a model to assess cognitive domains. The DMD gene variant location and its association with variable degrees of cognitive impairment necessitate identification of a common denominator. Computer architectures provide a framework to delineate the mechanisms involved in the cognitive functioning of the human brain. Copy number variations in the 79 exons of DMD gene were screened in 84 DMD subjects by Multiplex Ligation-dependent Probe Amplification (MLPA). DMD subjects were categorized based on the presence or absence of DP140 isoform. The cognitive and neuropsychological assessments were carried out as per inclusion criteria using standard scales. Instance-based learning theory (IBLT) based on the partial matching process was developed to mimic Stroop Color and Word Task (SCWT) performance on Adaptive Control of Thought-Rational (ACT-R) cognitive architecture based on IBLT. Genotype-phenotype correlation was conducted based on the mutation location in DMD gene. Assessment of specific cognitive domains in DP140 - ve group corresponded to the involvement of multiple brain lobes including temporal (verbal and visual learning and memory), parietal (visuo-conceptual and visuo-constructive abilities) and frontal (sustained and focused attention, verbal fluency, cognitive control). Working memory axis was found to be the central domain through tasks including RAVLT trial 1, recency effect, digit span backward, working memory index, arithmetic subtests in the Dp140 - ve group. IBLT validated the non-reliance of DMD subjects on recency indicating affected working memory domain. Modeling strategy revealed altered working memory processes in DMD cases with affected Dp140 isoform. DMD brain was observed to rely on primacy than the recency suggesting alterations in working memory capacity. Modeling revealed lowered activation of DMD brain with Dp140 - ve in order to retrieve the instances.

Cyber Security: Effects of Penalizing Defenders in Cyber-Security Games via Experimentation and Computational Modeling.

Cyber-attacks are deliberate attempts by adversaries to illegally access online information of other individuals or organizations. There are likely to be severe monetary consequences for organizations and its workers who face cyber-attacks. However, currently, little is known on how monetary consequences of cyber-attacks may influence the decision-making of defenders and adversaries. In this research, using a cyber-security game, we evaluate the influence of monetary penalties on decisions made by people performing in the roles of human defenders and adversaries via experimentation and computational modeling. In a laboratory experiment, participants were randomly assigned to the role of "hackers" (adversaries) or "analysts" (defenders) in a laboratory experiment across three between-subject conditions: Equal payoffs (EQP), penalizing defenders for false alarms (PDF) and penalizing defenders for misses (PDM). The PDF and PDM conditions were 10-times costlier for defender participants compared to the EQP condition, which served as a baseline. Results revealed an increase (decrease) and decrease (increase) in attack (defend) actions in the PDF and PDM conditions, respectively. Also, both attack-and-defend decisions deviated from Nash equilibriums. To understand the reasons for our results, we calibrated a model based on Instance-Based Learning Theory (IBLT) theory to the attack-and-defend decisions collected in the experiment. The model's parameters revealed an excessive reliance on recency, frequency, and variability mechanisms by both defenders and adversaries. We discuss the implications of our results to different cyber-attack situations where defenders are penalized for their misses and false-alarms.

Understanding Human Decision Making in an Interactive Landslide Simulator Tool via Reinforcement Learning.

Prior research has used an Interactive Landslide Simulator (ILS) tool to investigate human decision making against landslide risks. It has been found that repeated feedback in the ILS tool about damages due to landslides causes an improvement in human decisions against landslide risks. However, little is known on how theories of learning from feedback (e.g., reinforcement learning) would account for human decisions in the ILS tool. The primary goal of this paper is to account for human decisions in the ILS tool via computational models based upon reinforcement learning and to explore the model mechanisms involved when people make decisions in the ILS tool. Four different reinforcement-learning models were developed and evaluated in their ability to capture human decisions in an experiment involving two conditions in the ILS tool. The parameters of an Expectancy-Valence (EV) model, two Prospect-Valence-Learning models (PVL and PVL-2), a combination EV-PU model, and a random model were calibrated to human decisions in the ILS tool across the two conditions. Later, different models with their calibrated parameters were generalized to data collected in an experiment involving a new condition in ILS. When generalized to this new condition, the PVL-2 model's parameters of both damage-feedback conditions outperformed all other RL models (including the random model). We highlight the implications of our results for decision making against landslide risks.

AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures.

Both statistical and neural methods have been proposed in the literature to predict healthcare expenditures. However, less attention has been given to comparing predictions from both these methods as well as ensemble approaches in the healthcare domain. The primary objective of this paper was to evaluate different statistical, neural, and ensemble techniques in their ability to predict patients' weekly average expenditures on certain pain medications. Two statistical models, persistence (baseline) and autoregressive integrated moving average (ARIMA), a multilayer perceptron (MLP) model, a long short-term memory (LSTM) model, and an ensemble model combining predictions of the ARIMA, MLP, and LSTM models were calibrated to predict the expenditures on two different pain medications. In the MLP and LSTM models, we compared the influence of shuffling of training data and dropout of certain nodes in MLPs and nodes and recurrent connections in LSTMs in layers during training. Results revealed that the ensemble model outperformed the persistence, ARIMA, MLP, and LSTM models across both pain medications. In general, not shuffling the training data and adding the dropout helped the MLP models and shuffling the training data and not adding the dropout helped the LSTM models across both medications. We highlight the implications of using statistical, neural, and ensemble methods for time-series forecasting of outcomes in the healthcare domain.

The effect of report cards on the coverage of maternal and neonatal health care: a factorial, cluster-randomised controlled trial in Uttar Pradesh, India.

BACKGROUND: Report cards are a prominent strategy to increase the ability of citizens to express their view, improve public accountability, and foster community participation in the provision of health services in low-income and middle-income countries. In India, social accountability interventions that incorporate report cards and community meetings have been implemented at scale, attracting considerable policy attention, but there is little evidence on their effectiveness in improving health. We aimed to evaluate the effect of report cards, which contain information on village-level indicators of maternal and neonatal health care, and participatory meetings targeted at health providers and community members (including local leaders) on the coverage of maternal and neonatal health care in Uttar Pradesh, India. METHODS: We conducted a repeated cross-sectional, 2 × 2 factorial, cluster-randomised controlled trial, in which each cluster was a village (rural) or ward (urban). The clusters were randomly assigned to one of four groups: the provider group, in which we shared report cards and held participatory meetings with providers of maternal and neonatal health services; the community group, in which we shared report cards and held participatory meetings with community members (including local leaders); the providers and community group, in which report cards were targeted at both health providers and the community; and the control group, in which report cards were not shared with anyone. We generated these report cards by collating data from household surveys and shared the report cards with the recipients (as determined by their assigned groups) in participatory meetings. The primary outcome was the proportion of women who had at least four antenatal care visits (ie, attended a clinic or were visited at home by a health-care worker) during their last pregnancy. We measured outcomes with cross-sectional household surveys that were taken at baseline, at a first follow-up (after 8 months of the intervention), and at a second follow-up (21 months after the start of the intervention). Analyses were by intention to treat. This trial is registered with ISRCTN, number ISRCTN11070792. FINDINGS: We surveyed eligible women for the baseline survey between Jan 13, and Feb 5, 2015. We then randomly assigned 44 clusters to the provider group, 45 clusters to the community group, 45 clusters to the provider and community group, and 44 clusters to the control group. Report cards of collated survey data were provided to recipient groups, as per their random allocation, in October, 2015, and in September, 2016. We ran the first follow-up survey between May 16 and June 10, 2016. We ran the second follow-up survey between June 18 and July 18, 2017. We measured the primary outcome in 3133 women (795 in the provider group, 781 in the community group, 798 in the provider and community group, and 759 in the control group) who gave birth during implementation of the intervention, between Feb 1, 2016, and July 18, 2017 (the end of the second follow-up survey). The report card intervention did not significantly affect the proportion of women who had at least four antenatal care visits (provider vs non-provider: odds ratio 0·85, 95% CI 0·65-1·13; community vs non-community: 0·86, 0·65-1·13). INTERPRETATION: Maternal health report cards containing information on village performance, targeted at either the community or health providers, had no detectable effect on the coverage of maternal and neonatal health care. Future research should seek to understand how the content of information and the delivery of report cards affect the success of this type of social accountability intervention. FUNDING: Merck Sharp and Dohme.

Why people avoid prescribed medical treatment in India?

Avoidance in seeking prescribed medical treatment can result in adverse consequences. The study was conducted to find out the reasons to avoid prescribed medical treatment and associations with various socioeconomic variables in India. Data from the National Sample Survey Organisation 71st Round on "Key Indicators of Social Consumption: Health" (January and June 2014) have been used. Variables such as place of residence, social categories, religion, and socioeconomic status have been used to study the associations with the various reasons to avoid prescribed medical treatment. Nonseriousness about the ailment was found to be the primary reason for not seeking prescribed medical treatment. Lack of availability of medical facility, long-waiting time, and financial constraints were other important reasons. Understanding the socioeconomic differentials among the reasons why people avoid prescribed medical treatment is critical in improving the effectiveness of health-care facilities in India.

Comparative Assessment of Economic Burden of Disease in Relation to Out of Pocket Expenditure.

Background: The economic costs associated with morbidity pose a great financial risk on the population. Household's over-dependence on out-of-pocket (OOP) health expenditure and their inability to cope up with the economic costs associated with illness often push them into poverty. The current paper aims to measure the economic burden and resultant impoverishment associated with OOP health expenditure for a diverse set of ailments in India. Methods: Cross-sectional data from National Sample Survey Organization (NSSO) 71st Round on "Key Indicators of Social Consumption: Health" has been employed in the study. Indices, namely the payment headcount, payment gap, concentration index, poverty headcount and poverty gap, are defined and computed. The measurement of catastrophic burden of OOP health expenditure is done at 10% threshold level. Results: Results of the study reveal that collectively non-communicable diseases (NCDs) have higher economic and catastrophic burden, individually infections rather than NCDs such as Cardio Vascular Diseases and cancers have a higher catastrophic burden and resultant impoverishment in India. Ailments such as gastro-intestinal, respiratory, musco-skeletal, obstetrics, and injuries also have a substantial economic burden on population and push them below the poverty line. Results also show that despite the pro-poor concentration of infections, their economic burden is more concentrated among the wealthier consumption groups. Conclusion: The study concludes that universal health coverage through adequate provision of pooled resources for health care and community-based health insurance is critical to reduce the economic burden and impoverishment related to OOP health expenditure. Measures should also be instituted to insulate people from economic burden on morbidity, especially the NCDs.

Economic burden, impoverishment, and coping mechanisms associated with out-of-pocket health expenditure in India: A disaggregated analysis at the state level.

INTRODUCTION: The high share of out-of-pocket (OOP) health expenditure imposes an extreme financial burden on households, and they have to incur a substantial amount of expenditure to avail health care services. This study analyses the inter-state differentials in the economic burden of OOP health expenditure, resultant impoverishment impact, and sources of finance used as coping mechanisms. MATERIALS AND METHODS: The study is based on health expenditure survey, namely the 71st Round on "Key Indicators of Social Consumption in India: Health," (2014) conducted in India by the National Sample Survey Organisation. The study uses headcount, payment gap, and concentration index to measure the economic burden, impoverishment impact of OOP health expenditure, and the level of inequality. RESULTS: On the basis of results, the states can be divided into four distinct categories: (1) States with low economic burden and low poverty impact of OOP health expenditure, (2) low economic burden and high poverty impact of OOP health expenditure, (3) high economic burden and low poverty impact of OOP health expenditure, and (4) high economic burden and high poverty impact of OOP health expenditure. CONCLUSIONS: Inter-state differentials in OOP health expenditure and impoverishment need proper attention of the government especially the policy makers.

Process evaluation of a social franchising model to improve maternal health: evidence from a multi-methods study in Uttar Pradesh, India.

BACKGROUND: A prominent strategy to engage private sector health providers in low- and middle-income countries is clinical social franchising, an organisational model that applies the principles of commercial franchising for socially beneficial goals. The Matrika programme, a multi-faceted social franchise model to improve maternal health, was implemented in three districts of Uttar Pradesh, India, between 2013 and 2016. Previous research indicates that the intervention was not effective in improving the quality and coverage of maternal health services at the population level. This paper reports findings from an independent external process evaluation, conducted alongside the impact evaluation, with the aim of explaining the impact findings. It focuses on the main component of the programme, the "Sky" social franchise. METHODS: We first developed a theory of change, mapping the key mechanisms through which the programme was hypothesised to have impact. We then undertook a multi-methods study, drawing on both quantitative and qualitative primary data from a wide range of sources to assess the extent of implementation and to understand mechanisms of impact and the role of contextual factors. We analysed the quantitative data descriptively to generate indicators of implementation. We undertook a thematic analysis of the qualitative data before holding reflective meetings to triangulate across data sources, synthesise evidence, and identify the main findings. Finally, we used the framework provided by the theory of change to organise and interpret our findings. RESULTS: We report six key findings. First, despite the franchisor achieving its recruitment targets, the competitive nature of the market for antenatal care meant social franchise providers achieved very low market share. Second, all Sky health providers were branded but community awareness of the franchise remained low. Third, using lower-level providers and community health volunteers to encourage women to attend franchised antenatal care services was ineffective. Fourth, referral linkages were not sufficiently strong between antenatal care providers in the franchise network and delivery care providers. Fifth, Sky health providers had better knowledge and self-reported practice than comparable health providers, but overall, the evidence pointed to poor quality of care across the board. Finally, telemedicine was perceived by clients as an attractive feature, but problems in the implementation of the technology meant its effect on quality of antenatal care was likely limited. CONCLUSIONS: These findings point towards the importance of designing programmes based on a strong theory of change, understanding market conditions and what patients value, and rigorously testing new technologies. The design of future social franchising programmes should take account of the challenges documented in this and other evaluations.

Influence of an Intermediate Option on the Description-Experience Gap and Information Search.

Research shows that people tend to overweight small probabilities in description and underweight them in experience, thereby leading to a different pattern of choices between description and experience; a phenomenon known as the Description-Experience (DE) gap. However, little is known on how the addition of an intermediate option and contextual framing influences the DE gap and people's search strategies. This paper tests the effects of an intermediate option and contextual framing on the DE gap and people's search strategies, where problems require search for information before a consequential choice. In the first experiment, 120 participants made choice decisions across investment problems that differed in the absence or presence of an intermediate option. Results showed that adding an intermediate option did not reduce the DE gap on the maximizing option across a majority of problems. There were a large majority of choices for the intermediate option. Furthermore, there was an increase in switching between options due to the presence of the intermediate option. In the second experiment, 160 participants made choice decisions in problems like those presented in experiment 1; however, problems lacked the investment framing. Results replicated findings from the first experiment and showed a similar DE gap on the maximizing option in a majority of problems in both the absence and presence of the intermediate option. Again, there were a large majority of choices for the intermediate option. Also, there was an increase in switching between options due to the presence of the intermediate option. Meta-analyses revealed that the absence or presence of the intermediate option created certain differences in the strength of frequency and recency processes. Also, a single natural-mean heuristic model was able to account for the experimental results across both experiments. We discuss implications of our findings to consequential decisions made after information search.

Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions.

Research shows that people's wait-and-see preferences for actions against climate change are a result of several factors, including cognitive misconceptions. The use of simulation tools could help reduce these misconceptions concerning Earth's climate. However, it is still unclear whether the learning in these tools is of the problem's surface features (dimensions of emissions and absorptions and cover-story used) or of the problem's structural features (how emissions and absorptions cause a change in CO2 concentration under different CO2 concentration scenarios). Also, little is known on how problem's difficulty in these tools (the shape of CO2 concentration trajectory), as well as the use of these tools as a decision aid influences performance. The primary objective of this paper was to investigate how learning about Earth's climate via simulation tools is influenced by problem's surface and structural features, problem's difficulty, and decision aids. In experiment 1, we tested the influence of problem's surface and structural features in a simulation called Dynamic Climate Change Simulator (DCCS) on subsequent performance in a paper-and-pencil Climate Stabilization (CS) task (N = 100 across four between-subject conditions). In experiment 2, we tested the effects of problem's difficulty in DCCS on subsequent performance in the CS task (N = 90 across three between-subject conditions). In experiment 3, we tested the influence of DCCS as a decision aid on subsequent performance in the CS task (N = 60 across two between-subject conditions). Results revealed a significant reduction in people's misconceptions in the CS task after performing in DCCS compared to when performing in CS task in the absence of DCCS. The decrease in misconceptions in the CS task was similar for both problems' surface and structural features, showing both structure and surface learning in DCCS. However, the proportion of misconceptions was similar across both simple and difficult problems, indicating the role of cognitive load to hamper learning. Finally, misconceptions were reduced when DCCS was used as a decision aid. Overall, these results highlight the role of simulation tools in alleviating climate misconceptions. We discuss the implication of using simulation tools for climate education and policymaking.