Independent Evaluation Research Articles

Statement by the Managing Director on the Independent Evaluation Office Report on the Evolving Application of the IMF’s Mandate

Statement by the Managing Director on the Independent Evaluation Office Report on the Evolving Application of the IMF’s Mandate

Learning in Lending: The World Bank’s Organisational Learning Journey in Albanian Health Financing Reform Projects, 1994–2022

Abstract Fostering a learning culture is crucial for aid and development organisations to address the complexities and uncertainties in their operating environments. This case study examines the World Bank’s learning processes in lending projects, focusing on the Albanian health financing reforms from 1994 to 2022. Using the Learning in Lending conceptual framework developed by the World Bank’s Independent Evaluation Group, the qualitative analysis of World Bank documents and reports offers valuable insights into the dynamics of learning processes in health projects. The findings illustrate a feedback loop process encompassing three distinct steps and reveal three types of learning: learning into lending, learning while lending, and learning from lending. By shedding light on the intricacies of the World Bank’s learning processes in health lending projects, this study not only deepens our understanding of how the organisation learns from its experience but also highlights the shortcomings and limitations of the process. Moreover, it emphasises the dual value of learning for multilateral and aid organisations, such as the World Bank, namely improving project results and addressing challenging developmental issues.

A multi-modal artificial intelligence model trained on H&E tissue images and clinical patient data for prediction of BRAF mutations in colorectal cancer.

e15161 Background: Analyzing genetic mutations is essential for personalized therapy decisions in oncology and beyond. In colorectal cancer (CRC), mutations in BRAF ( BRAFmut) render patients eligible for targeted treatment with BRAF inhibitors and immunotherapy. BRAFmut is routinely analyzed via genomic sequencing, which, however, is costly and does not provide diagnostic results immediately. Reliable detection of BRAFmut status from histological images using artificial intelligence (AI) could provide a more accessible and faster diagnostic alternative. Previous approaches to image-based BRAFmut analysis, however, failed to reach a critical level of accuracy on independent test cohorts, with reported area under the receiver operating characteristic curve (AUROC) values typically below 0.8. Methods: We hypothesized that combining tissue image analysis with clinical patient data could improve diagnostic accuracy for BRAFmut. Following our hypothesis, we developed a multi-modal AI model on n = 426 CRC cases from the TCGA database using BRAFmut data, H&E-stained tissue images, and information on patient parameters that are readily available in clinical practice and that are known to be associated with BRAFmut frequency. The model was based on a vision transformer architecture incorporating pathology-specific information using a pretrained embedding model for encoding tissue image features. Model performance was validated on an independent hold-out TCGA cohort of n = 107 samples and an additional external cohort of n = 104 CRC samples from the CPTAC database. Results: Our multi-modal AI model reached an AUROC of 0.85 ± 0.02 on the TCGA hold-out test set. Notably, on the second, external CPTAC dataset the predictive accuracy level was similar (AUROC of 0.83 ± 0.02) indicating the robustness of our approach to generalize to different datasets. Additional analyses showed that the significant performance boost over previous approaches was gained by the multi-modal analysis setup, as predicting BRAFmut based on either tissue, or clinical data alone led to lower accuracy values. Explainability analysis of AI results confirmed that relevant biological features on the histological slides, mainly tumor areas, were considered by the model for decision making. Conclusions: The combination of H&E images with clinically readily available patient parameters enabled us to develop a model predicting BRAFmut in CRC that surpasses previous approaches in diagnostic accuracy on independent evaluation cohorts. This indicates that combining histological tissue information with clinical parameters can increase the ability of AI models to predict genetic variants from tissue and more generally highlights the potential of multi-modal deep learning approaches to enhance predictive AI models in digital pathology.

The Chair’s Summing Up Independent Evaluation Office—The Evolving Application of the IMF’s Mandate

The Chair’s Summing Up Independent Evaluation Office—The Evolving Application of the IMF’s Mandate

Evaluation of the b Maps on the Faults of the Major (M > 7) South California Earthquakes

AbstractWe use the Godano et al. (2022, https://doi.org/10.1029/2021ea002205) method for evaluating the b maps of the faults associated with the largest earthquakes M ≥ 7.0 that occurred in California. The method allows an independent evaluation of the b parameter, avoiding the overlap of the cells and the omission of some earthquakes, while keeping all the available information in the catalog. We analyzed four large earthquakes: Landers, Hector Mine, Baja California, and Searles Valley. The maps obtained confirm that the b value can be considered as a strain meter and allow us to elucidate the presence of barriers, such as obstacles to the propagation of the fracture, on the fault of the analyzed earthquakes. A further estimated parameter is the time window during which aftershocks occur in the cell, Δt. This quantity is very useful for a better definition of the aftershock generation mechanism. It reveals where the stress is released in a short time interval and how the complexity of the faulting process controls the occurrence of aftershocks on the fault, and also the duration of the entire sequence.

Randomized waitlist-controlled trial of buried in treasures facilitated support groups and in-home uncluttering (BIT+) for hoarding disorder

ObjectiveThe Buried in Treasures (BIT) workshop is a promising treatment for hoarding disorder (HD), though many participants struggle with home uncluttering. This randomized waitlist-controlled trial investigated the efficacy of a version of BIT, augmented with in-home uncluttering practice (BIT+). MethodAdults (N = 41) with hoarding disorder were recruited from the community and randomly assigned to BIT+ or waitlist. BIT+ consisted of 16 sessions of the BIT workshop and 10 uncluttering home visits over 18 weeks. Outcome measures included the Saving Inventory-Revised (self-report) and the Clutter Image Rating Scale (self and independent evaluator rated). Between group repeated measures analyses using general linear modeling examined the effect of BIT+ vs waitlist control on hoarding symptoms after 18 weeks. Within group analyses examined pre-post effects for all BIT+ participants combined after 18 weeks. ResultsAfter 18 weeks, BIT+ participants benefited significantly more than waitlist controls on hoarding severity with large effect size (Cohen's d = 1.5, p < .001). BIT+ was also associated with improvement reductions in hoarding symptoms, clutter, and functional impairment. ConclusionsThe BIT+ intervention offers promise as a treatment option for hoarding. Adding in-home uncluttering practice may incrementally improve discarding practices. Future controlled trials are warranted.

A Method to Quantitative Compare Obfuscating Ttransformations

The paper considers the problem of quantitative comparison of potency and resistance of practically applied obfuscating transformations of program code. A method is proposed to find the potency and resistance of transformations by calculating the «comprehensibility» of the obfuscated and deobfuscated versions of a program, respectively. As a measure of program comprehensibility, it is proposed to use the similarity of this program to the approximation of its «most comprehensible» version. Based on the proposed method a model to assess potency and resistance was built, the main elements of which are: a set of investigated obfuscating transformations, a similarity function, a method to approximate the most comprehensible version of the program and a deobfuscator. To implement this model 1) obfuscating transformations provided by Hikari obfuscator are chosen; 2) 8 similarity functions are constructed by machine learning methods using static characteristics of programs from CoreUtils, PolyBench and HashCat sets; 3) the smallest program version was chosen as an approximation of the most comprehensible program version (found among the versions obtained using optimization options of GCC, Clang and AOCC compilers); 4) a program deobfuscation scheme based on the optimizing compiler from LLVM was built and implemented. The results of the potency and resistance for sequences of transformations of lengths one, two and three were experimentally obtained. These results showed consistency with the results of independent potency and resistance evaluations obtained by other methods. In particular, it was found that the highest potency and resistance are demonstrated by sequences of transformations starting with transformations of the control flow graph, and the lowest resistance and potency are generally demonstrated by sequences that do not contain such transformations.

Evaluating Commercial Loop-Mediated Isothermal Amplification Master Mixes for Enhanced Detection of Foodborne Pathogens.

Loop-mediated isothermal amplification, LAMP, is nowadays the most popular isothermal nucleic acid amplification technique, and as such, several commercial, ready-to-use master mixes have flourished. Unfortunately, independent studies to determine their performance are limited. The current study performed an independent evaluation of the existing ready-to-use commercial LAMP master mixes WarmStart® LAMP Kit, LavaLAMP™ DNA Master Mix, Saphir Bst Turbo GreenMaster, OptiGene Fast Master Mix ISO-004, and SynLAMP Mix. To reduce bias, three different genes, namely ttr (Salmonella spp.), rfbE (E. coli O157), and hly (Listeria monocytogenes), were targeted. The comparison was based on amplification speed, performance with decreasing DNA concentrations, and the effect of five typical LAMP reaction additives (betaine, DMSO, pullulan, TMAC, and GuHCl). Significant differences were observed among the different master mixes. OptiGene provided the fastest amplification and showed less detrimental effects associated with the supplements evaluated. Out of the chemicals tested, pullulan provided the best results in terms of amplification speed. It is noteworthy that the different additives impacted the master mixes differently. Overall, the current study provides insights into the performance of commercial LAMP master mixes, which can be of value for the scientific community to better select appropriate reagents when developing new methods.

Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies

SARS-CoV-2′s global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers.

Parent-coached exposure therapy versus cognitive behavior therapy for childhood anxiety disorders

Cognitive-behavioral therapy (CBT) is the strongest evidenced-based therapy for childhood anxiety disorders (CADs). However, CBT’s impact is limited by its lack of clear superiority over treatment as usual, excessive length, and greater than 50% of patients remaining symptomatic. Parent-coached exposure therapy (PCET) is designed to treat CADs more effectively and efficiently through a focus on exposure and working with parents and youth together. In a randomized controlled trial, 78 patients (78% female) aged 7 to 17 with CADs were assigned to PCET or the gold-standard CBT. The primary outcome was independent evaluator ratings of anxiety severity at mid- and post-treatment. Secondary outcomes were parent- and child-reported symptoms. Patients receiving PCET had significantly lower mean scores than those receiving CBT on the primary outcome measure at mid-treatment (3.03 ± 0.14, 95% CI, 2.75–3.32 vs. 3.77 ± 0.16 95% CI, 3.45–4.08, p = 0.0010) and post-treatment (2.79 ± 0.14, 95% CI, 2.50–3.07 vs. 3.33 ± 0.16, 95% CI, 2.02–3.64, p = 0.0153). Similar significant results were found with the secondary parent- and child-reported outcomes. These superior results were achieved in PCET with fewer sessions (6.62, SD = 2.8) than those in CBT (8.00, SD = 3.1), p = 0.041. The superior effectiveness and efficiency of PCET likely results from the greater focus on implementing exposure exercises compared to traditional CBT.

Health Technology Access and Peer Support Among Digitally Engaged People Experiencing Homelessness: Qualitative Study.

Although the effects of digital health are receiving wide scientific attention, very little is known about the characteristics of digitally engaged people experiencing homelessness, especially in Central and Eastern Europe. Our previous research revealed a considerable level of internet use in the homeless population of Budapest, Hungary, for general purposes (350/662, 52.9%) and medical purposes (229/664, 34.6%). Moreover, a digitally engaged subgroup was identified (129/662, 19.5%). The aim of this exploratory study was to map out the resources, attitudes, and behaviors of digitally engaged homeless individuals in relation to digital technology to set the basis for potential health policy interventions, which will enable better access to health services through strengthening of the digital components of the existing health care system. Between August 18, 2022, and October 27, 2022, a total of 12 in-depth semistructured interviews were conducted in 4 homeless shelters in Budapest, Hungary. Upon analysis by 3 independent evaluators, 2 interviews were excluded. The interviewees were chosen based on purposive sampling with predefined inclusion criteria. Thematic analysis of the transcripts was conducted. In the thematic analysis, 4 main themes (attitude, access, usage patterns, and solutions for usage problems) emerged. Health-related technology use mostly appeared in health information-seeking behavior. Online search for prescribed medications (5 interviews), active ingredients of medications (4 interviews), medicinal herbs believed to replace certain pills (2 interviews) or foods, and natural materials (1 interview) were mentioned. Moreover, mobile health app use (3 interviews) was reported. The intention to circumvent or check on mainstream health care solutions was mainly associated with previous negative experiences in the health care system. Several gaps in the daily use of technology were identified by the interviewees; however, more than half of the interviewees (6/10) turned out to be contact points for their peers for digital problem-solving or basic digital literacy skill enhancement in the homeless shelters. Furthermore, a lack of institutional support or special programs targeting senior clients was noted. Digitally engaged homeless individuals might become mediators between their peers and comprehensive digital health programs. They have the trust of their peers, can recognize and harness the benefits of digital technology, and are able to provide meaningful help in technology- and usage-related issues through experience. Digital health services have great promise in community shelters for managing and preventing health issues, and digitally engaged individuals might be important for the success of such services.

Patients as teachers: a within-subjects randomized pilot experiment of patient-led online learning modules for health professionals

PurposeMany health professions education programs involve people with lived experience as expert speakers. Such presentations may help learners better understand the realities of living with chronic illness or experiencing an acute health problem. However, lectures from only one or a small number of people may not adequately illustrate the perspectives and experiences of a diverse patient cohort. Additionally, logistical constraints such as public health restrictions or travel barriers may impede in-person presentations, particularly among people who have more restrictions on their time. Health professions education programs may benefit from understanding the potential effects of online patient-led presentations with a diverse set of speakers. We aimed to explore whether patient-led online learning modules about diabetes care would influence learners’ responses to clinical scenarios and to collect learners’ feedback about the modules.MethodThis within-subjects randomized experiment involved 26 third-year medical students at Université Laval in Quebec, Canada. Participation in the experiment was an optional component within a required course. Prior to the intervention, participating learners responded to three clinical scenarios randomly selected from a set of six such scenarios. Each participant responded to the other three scenarios after the intervention. The intervention consisted of patient-led online learning modules incorporating segments of narratives from 21 patient partners (11 racialized or Indigenous) describing why and how clinicians could provide patient-centered care. Working with clinical teachers and psychometric experts, we developed a scoring grid based on the biopsychosocial model and set 0.6 as a passing score. Independent evaluators, blinded to whether each response was collected before or after the intervention, then scored learners’ responses to scenarios using the grid. We used Fisher’s Exact test to compare proportions of passing scores before and after the intervention.ResultsLearners’ overall percentage of passing scores prior to the intervention was 66%. Following the intervention, the percentage of passing scores was 76% (p = 0.002). Overall, learners expressed appreciation and other positive feedback regarding the patient-led online learning modules.DiscussionFindings from this experiment suggest that learners can learn to provide better patient-centered care by watching patient-led online learning modules created in collaboration with a diversity of patient partners.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ РЕЗУЛЬТАТОВ ЛЕЧЕНИЯ ОСТЕОХОНДРАЛЬНЫХ ДЕФЕКТОВ СУСТАВНОЙ ПОВЕРХНОСТИ КОЛЕННОГО СУСТАВА, АССОЦИИРОВАННЫХ С ПОВРЕЖДЕНИЕМ ПЕРЕДНЕЙ КРЕСТООБРАЗНОЙ СВЯЗКИ

Purpose of work: to perform a comparative analysis of the results of treatment of patients with osteochondral defects of the articular surface associated with damage of the anterior cruciate ligament of the knee joint. Materials and Methods. Between 2015 and 2023 inclusive, 72 patients with anterior cruciate ligament injury accompanied by osteochondral defects of femoral condyles were treated. All patients were randomly divided into three groups: the first group - simultaneous ACL reconstruction and abrasive chondroplasty (ACh-ACL group) (n=24); the second group - ACL reconstruction and microfracturing (MF-ACL group) (n=24); the third group - ACL reconstruction and mosaic autochondroplasty (OAT-ACL group) (n=24). For the purpose of independent evaluation, a control group - anterior cruciate ligament reconstruction with intact articular cartilage (IAC-ACL group) (n=24) was introduced for comparison. Results and Discussion. Having analyzed the results of IKDC-2000 and Lysholm + Tegner questionnaires at 3, 6 and 12 months after the operation we noted the greatest positive dynamics in the early postoperative period (3 months) in all groups. From 6 to 12 months the dynamics changed its tendency and more pronouncedly passed into a "plateau" in groups 1 and 2, which was clinically determined by the restoration of passive movements with insufficient volume of active flexion-extension movements in the knee joint, as well as by the persistence of pain syndrome during loading. Conclusions. Simultaneous reconstruction of osteochondral lesions of the articular surface of the knee joint and reconstruction of the anterior cruciate ligament is the basis for full restoration of the function and bearing capacity of the knee joint. In this case, the use of organ-reconstructive methods of surgical intervention (bone-cartilage autochondroplasty and anterior cruciate ligament reconstruction (OAT-ACL)) allows achieving better clinical results compared to "mesenchymal stimulation" of the defect area, showing full functional recovery of the knee joint.

Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches

The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM mapping with improved accuracy. However, this integration remains largely underexplored. In this context, the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficients for global-scale SSM estimation was investigated, by fully considering the potential error sources in conventional radiative transfer models (RTMs) as well as other SSM linked factors. Based on ground measurements from globally distributed dense networks with mitigated mismatch issues and spatial/temporal independent evaluation strategies, this study: (i) comprehensively evaluated four classical machine learning approaches, including Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN), and chose the best performing RF method to implement the final integration of SSM; (ii) compared the integration retrievals to those made using data from a single sensor (SMAP or ASCAT) with the same machine learning framework, as well as to the SMAP passive, ASCAT active, and ESA CCI active-passive combined SSM products. The results show the integration retrievals achieve satisfactory performance by obtaining an averaged unbiased root mean squared error (ubRMSE) of 0.042 m3/m3 and a temporal correlation of 0.756, which are superior to machine learning based SSM estimated from a single active or passive sensor, and also outperform the SMAP, ASCAT, and ESA CCI products. Moreover, the temporal resolution is evidently improved compared to the SMAP and ASCAT SSM products, with a temporal ratio exceeding 60% for most areas across the globe. Therefore, blending active and passive measurements affords a more reliable SSM mapping with increased sampling at the global scale, and could contribute to improved hydro-ecological applications.

Neurocognitive functioning in adults with trichotillomania: Predictors of treatment response and symptom severity in a randomized control trial

Trichotillomania (TTM) is associated with impairments in response inhibition and cognitive flexibility, but it is unclear how such impairments relate to treatment outcome. The present study examined pre-treatment response inhibition and cognitive flexibility as predictors of treatment outcome, change in these domains from pre-to post-treatment, and associations with TTM severity. Participants were drawn from a randomized controlled trial comparing acceptance-enhanced behavior therapy (AEBT) to psychoeducation and supportive therapy (PST) for TTM. Adults completed assessments at pre-treatment (n = 88) and following 12 weeks of treatment (n = 68). Response inhibition and cognitive flexibility were assessed using the Stop Signal Task and Object Alternation Task, respectively. Participants completed the MGH-Hairpulling Scale. Independent evaluators administered the NIMH-Trichotillomania Severity Scale and Clinical Global Impressions-Improvement Scale. Higher pre-treatment TTM severity was associated with poorer pre-treatment cognitive flexibility, but not response inhibition. Better pre-treatment response inhibition performance predicted positive treatment response and lower post-treatment TTM symptom severity, irrespective of treatment assignment. Cognitive flexibility did not predict treatment response. After controlling for age, neither neurocognitive variable changed during treatment. Response inhibition and cognitive flexibility appear uniquely related to hair pulling severity and treatment response in adults with TTM. Implications for treatment delivery and development are discussed.

Effects of olfactory and/or gustatory stimuli on feeding of preterm infants: A systematic review and meta-analysis.

To evaluate the effect of olfactory and/or gustatory stimulation interventions on feeding outcomes in preterm infants. We conducted systematic searches across various academic databases, including PubMed, Embase, Web of Science, the Cochrane Library, the Chinese Biomedical Literature Service System, China National Knowledge Infrastructure, the Wanfang Database, and the Wipu Database. These searches aimed to identify randomized controlled trials investigating the impact of olfactory and/or gustatory stimulation on preterm infants. The search period spanned from the inception of the databases until December 2022. Two independent evaluators autonomously reviewed the literature, extracted pertinent data, assessed the quality of the included studies, and conducted a meta-analysis using RevMan 5.3 software. A total of 7 randomized controlled trials or quasi-experimental studies were included, with a total of 871 participants. Olfactory and gustatory stimulation demonstrated a reduction in the time to full enteral feeds in preterm infants when compared to usual care (MD = -1.60 days; 95% CI = -2.31, -0.89; p<0.0001). No substantial evidence was identified regarding the influence of olfactory and gustatory stimulation on the duration of gastric tube placement, length of hospitalization, incidence of necrotizing enterocolitis, or occurrence of spontaneous bowel perforation in preterm infants. Olfactory and gustatory stimulation show potential benefits for preterm infants. However, due to the low to very low level of certainty associated with the available data, our ability to assess the effects is limited. Further trials and studies are essential to enhance our understanding of the mechanisms and effectiveness of olfactory and gustatory stimulation therapies.

Using spirometry for screening and diagnosis of chronic respiratory diseases in primary health care: findings from a community health project in rural India

Background Chronic respiratory diseases (CRDs) such as Chronic obstructive pulmonary disease (COPD), Asthma and post-tuberculosis lung disease (PTLD) are a growing public health concern in India. Early diagnosis and management of CRDs require a good quality spirometry test, a technician to conduct spirometry and a chest physician to interpret the results. In India, these are not available at the primary care level. This study reports the feasibility of a large-scale CRD diagnosis and management program in primary care using a unique point-of-care spirometry solution, Briota PFT in a Box™. # Methods A community-based cross-sectional study was conducted among 15,602 adults in Dindori Taluka (subdivision), Nashik, Maharashtra state. This study was part of a holistic CRD diagnosis and management program SAVE™ (Spirometry Assisted Virtually Early). Make In India point of care solution Briota PFT in a Box™ was used. A House‐to‐house community-based assessment checklist (CBAC) survey, 4-parameter spirometry test, 15-parameter pre and post-bronchodilator spirometry test and a software-assisted medical examination by the medical officer at primary care were conducted. Software was used to generate CRD diagnosis and lung health score (LHS™). The diagnosis was verified by the chest physician. Confirmed diagnosed patients were provided treatment and offered a patient support program. Interviews were conducted with the patients, nurses, doctors and public health officials for understanding feasibility and documenting learning from program SAVE™. # Results Out of 15,602 adults surveyed, total 4,937 (31.6%) were identified as “CRD high risk”. 1231 participants based on medical examination, spirometry tests and software analysis were identified as CRD candidates by medical officers at primary care. 1154 participants out of 15,602 (7.4%) were confirmed diagnosed as CRD patients post independent evaluation by chest physicians. At the time of follow-up, 537 patients (75% of 712 patients enrolled in patient support program) reported improvement in symptoms and high satisfaction with the program. District health officer, Medical officers, nurses, Accredited Social Health Activist (ASHA) from the primary health care centers confirmed ease of use and feasibility of using Briota PFT in a Box™ in program SAVE™. Outcome and learning from program SAVE™ was documented and submitted to Ministry of Health and Family Welfare Government of India. # Conclusions CRD diagnosis and management in large scale settings in primary healthcare level using a point of care spirometry solution Briota PFT in a Box™ is highly feasible.

Evaluating bias and noise induced by the U.S. Census Bureau's privacy protection methods.

The U.S. Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct an independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm used for the 2020 Census and the swapping algorithm implemented for the three previous Censuses. Our evaluation leverages the Noisy Measurement File (NMF) as well as two independent runs of the TopDown algorithm applied to the 2010 decennial Census. We find that the NMF contains too much noise to be directly useful without measurement error modeling, especially for Hispanic and multiracial populations. TopDown's postprocessing reduces the NMF noise and produces data whose accuracy is similar to that of swapping. While the estimated errors for both TopDown and swapping algorithms are generally no greater than other sources of Census error, they can be relatively substantial for geographies with small total populations.

Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance

Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance

Effectiveness of the Application of VR Technology in Basic Nursing Education for the Acquisition of Parenting Skills in Japan

Background: Through virtual reality (VR) technology, one can be guided through physical actions while viewing images that are equivalent to an actual environment. Because VR environments ensure safety and provide a repetitive learning environment, educational methods that use VR technology are being developed for nursing education.Purpose: To create a parenting skills program that utilizes VR technology and compare participants’ degree of independence in their parenting skills before and after the program.Methods: The researcher filmed with a digital camera using a 360-degree lens, edited the filmed video into a 5-minute program, and created a VR parenting skills program. Participants’ degree of parenting independence and subjective evaluation of sickness due to the use of VR were assessed before and after the VR parenting skills program.Results: About a third of the participants had experienced caring for a newborn and had little experience bathing them. In FY2021, the VR parenting skills program was improved in FY2020, and when comparing before and after the VR parenting skills program, participants’ level of independence in their parenting skills increased. Simulator sickness questionnaires scores before and after viewing VR did not differ significantly.Conclusion: After viewing the program, participants’ degree of independence in their nursing skills increased. In addition, because VR technology has little effect on the body, we believe this is an effective educational method for learning nursing skills.