Barrier to Utilization of Primary Healthcare Resources in Children Two Years of Age and Under

This is a retrospective longitudinal study of health service utilization using the Immunization Practice Data Set obtained from the Center for Pediatric Research, Norfolk, Virginia. Cluster sampling was used to identify a cohort of children (n = 271), aged 0 to 24 months, from the city of Norfolk, Virginia. A two-year abstraction of medical records was used to determine utilization practice patterns for three categories of health: well-baby, acute and chronic care. The purpose of this study was to identify socio-demographic, need, and health system factors associated with under-utilization of primary care services using Aday\u27s model. The proportion of children who met the American Academy of Pediatrics guidelines for the number of preventive care visits was identified. As a group, half of the children in the first year-of-life failed to meet the AAP guidelines of 5 well-baby visits (mean = 4.64, SD 2.72) while most of those in the second year-of-life slightly exceeded the 3 well-baby visit standard (mean = 4.04, SD 2.47). Compliance in the latter group was 76.60 of the cohort (n = 82) and those that did not meet the AAP specification were 23.4% (n = 25). The mean age of the child at first visit was 8.1 months and represents a highly significant lack of well baby/preventive care visits during the first year of life in the study cohort. Results of this study concur with prior research in identifying risk factors/variables associated with access to healthcare providers and under utilization of primary care providers. Respondents who tended to under-utilize primary care services and over utilize emergency care providers were Black, single unsupported parents, from low-income families, with low education and those who lacked insurance. The major barriers reported by parents were location, lack of transportation, and cost

Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

Machine Translation from Standard German to Alemannic Dialects

Machine translation has been researched using deep neural networks in recent years. These networks require lots of data to learn abstract representations of the input stored in continuous vectors. Dialect translation has become more important since the advent of social media. In particular, when dialect speakers and standard language speakers no longer understand each other, machine translation is of rising concern. Usually, dialect translation is a typical low-resourced language setting facing data scarcity problems. Additionally, spelling inconsistencies due to varying pronunciations and the lack of spelling rules complicate translation. This paper presents the best-performing approaches to handle these problems for Alemannic dialects. The results show that back-translation and conditioning on dialectal manifestations achieve the most remarkable enhancement over the baseline. Using back-translation, a significant gain of +4.5 over the strong transformer baseline of 37.3 BLEU points is accomplished. Differentiating between several Alemannic dialects instead of treating Alemannic as one dialect leads to substantial improvements: Multi-dialectal translation surpasses the baseline on the dialectal test sets. However, training individual models outperforms the multi-dialectal approach. There, improvements range from 7.5 to 10.6 BLEU points over the baseline depending on the dialect

Nonoperative Treatment of Charcot Neuro-osteoarthropathy

Conservative treatment of Charcot neuro-osteoarthropathy (CN) aims to retain a stable, plantigrade, and ulcer-free foot, or to prevent progression of an already existing deformity. CN is treated with offloading in a total contact cast as long as CN activity is present. Transition to inactive CN is monitored by the resolution of clinical activity signs and by resolution of bony edema in MRI. Fitting of orthopedic depth insoles, orthopedic shoes, or ankle-foot orthosis should follow immediately after offloading has ended to prevent CN reactivation or ulcer development. Keywords: Charcot arthropathy; Charcot foot; Charcot neuro-osteoarthropathy; Conservative; Management; Nonoperative; Treatmen

Synthesis and Encapsulation of Uniform Star‐Shaped Block‐Macromolecules

Linear uniform oligomers synthesized via a two‐step iterative cycle are postmodified with uniform octaethylene glycol monomethyl ether and finally coupled via azide‐alkyne cycloaddition to yield uniform star‐shaped block macromolecules with a mass ranging from 10 to 14 kDa. Each of the molecules is carefully characterized by NMR, electrospray ionization mass spectrometry (ESI‐MS), and size exclusion chromatography (SEC) to underline their purity as well as their uniformity. The obtained star‐shaped macromolecules are investigated in their ability to encapsulate dye molecules by carrying out qualitative solid–liquid phase transfer experiments

DaCToR: A data collection tool for the RELATER project

Collecting domain-specific data for under-resourced languages, e.g., dialects of languages, can be very expensive, potentially financially prohibitive and taking long time. Moreover, in the case of rarely written languages, the normalization of non-canonical transcription might be another time consuming but necessary task. In order to collect domain-specific data in such circumstances in a time and cost-efficient way, collecting read data of pre-prepared texts is often a viable option. In order to collect data in the domain of psychiatric diagnosis in Arabic dialects for the project RELATER, we have prepared the data collection tool DaCToR for collecting read texts by speakers in the respective countries and districts in which the dialects are spoken. In this paper we describe our tool, its purpose within the project RELATER and the dialects which we have started to collect with the tool

One‐Pot Synthesis of Thiocarbamates

An efficient isocyanide-based synthesis of S-thiocarbamates was discovered and thoroughly investigated. The new reaction protocol is a one-pot procedure and allows the direct conversion of N-formamides into thiocarbamates by initial dehydration with p-toluene sulfonyl chloride to the respective isocyanide and subsequent addition of a sulfoxide component. Contrary to recent literature, which also uses isocyanides as starting material, but with other sulfur reagents than sulfoxides, in this protocol, no isolation and purification of the isocyanide component is necessary, thus significantly decreasing the environmental impact and increasing the efficiency of the synthesis. The new protocol was applied to synthesize a library of sixteen thiocarbamates, applying four N-formamides and four commercially available sulfoxides. Furthermore, experiments were conducted to investigate the reaction mechanism. Finally, four norbornene-based thiocarbamate monomers were prepared and applied in controlled ring-opening metathesis polymerization (ROMP) reactions. The polymers were characterized by size-exclusion chromatography (SEC) and their properties were investigated utilizing differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA)