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Self-determination is a complex process with several components, such as making choices and independence. However, limited research on self-determination has focused on individuals with moderate to severe autism spectrum disorder. This study aimed to support independent leisure engagement and choice-making of leisure activities for this population by using a visual support intervention package, including the use of a visual activity schedule, video modeling, structured environment, preference assessment, and prompt procedures. The study used a single case research multiple baseline probe design to test the effectiveness of the intervention on three adults with autism spectrum disorder. Findings showed that implementing the visual support package was effective in promoting independent leisure engagement and facilitating choice-making of leisure activities. The study highlights the need for and advantages of incorporating video modeling and preference assessment into the visual activity schedule for this particular population.

BackgroundIt is expected that COVID-19 vaccines will become available in China by the end of 2020. Vaccinating children against COVID-19 would contribute to the control of the pandemic and the recovery of the global economy. For children under the age of 18 years, parents are usually the decision makers regarding their children’s vaccination. ObjectiveThe goal of this study was to investigate parental acceptability of free COVID-19 vaccination for children under the age of 18 years in China. MethodsThis is a secondary analysis of a cross-sectional, closed online survey among 2053 factory workers in Shenzhen, China, implemented from September 1 to 7, 2020. Participants of the online survey were full-time employees aged 18 years or over who had resumed work in factories in Shenzhen. Factory workers in Shenzhen are required to receive physical examinations once a year. Eligible workers attending six designated physical examination sites were invited to complete an online survey. This study was based on a subsample of those who had at least one child under the age of 18 years (N=1052). After being briefed that COVID-19 vaccines developed by China are likely to be available by the end of 2020, participants were asked about their likelihood of having their children under the age of 18 years take up free COVID-19 vaccination provided by the government, if it existed. Multivariate logistic regression models were fitted to examine the associations of perceptions related to COVID-19 vaccination based on the theory of planned behavior (TPB) and exposure to information related to COVID-19 through social media with parental acceptability, after controlling for significant background characteristics. ResultsThe prevalence of parents’ acceptability of COVID-19 vaccination for their children was 72.6% (764/1052). After adjusting for significant background characteristics, positive attitudes toward COVID-19 vaccination (adjusted odds ratio [AOR] 1.70, 95% CI 1.50-1.91), the perception that a family member would support them in having their children take up COVID-19 vaccination (ie, perceived subjective norm) (AOR 4.18, 95% CI 3.21-5.43), and perceived behavioral control to have the children take up COVID-19 vaccination (AOR 1.84, 95% CI 1.49-2.26) were associated with higher parental acceptability of COVID-19 vaccination. Regarding social media influence, higher exposure to positive information related to COVID-19 vaccination was associated with higher parental acceptability of COVID-19 vaccination (AOR 1.35, 95% CI 1.17-1.56). Higher exposure to negative information related to COVID-19 vaccination was negatively associated with the dependent variable (AOR 0.85, 95% CI 0.74-0.99). ConclusionsParents’ acceptability of COVID-19 vaccination for their children under 18 years of age was high in China. The TPB is a useful framework to guide the development of future campaigns promoting COVID-19 vaccination targeting parents. Transparency in communicating about the vaccine development process and vaccine safety testing is important. Public health authorities should also address misinformation in a timely manner.

BackgroundPredictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. ObjectiveThis study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. MethodsResearch articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. ResultsOf the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). ConclusionsPrediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial RegistrationPROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

BackgroundBreast cancer has become one of the most frequently diagnosed carcinomas and the leading cause of cancer deaths. The substantial growth in the number of breast cancer patients has put great pressure on health services. Meanwhile, the information patients need has increased and become more complicated. Therefore, a comprehensive and in-depth understanding of their information needs is urgently needed to improve the quality of health care. However, previous studies related to the information needs of breast cancer patients have focused on different perspectives and have only contributed to individual results. A systematic review and synthesis of breast cancer patients’ information needs is critical. ObjectiveThis paper aims to systematically identify, evaluate, and synthesize existing primary qualitative research on the information needs of breast cancer patients. MethodsWeb of Science, EBSCO, Scopus, ProQuest, PubMed, PsycINFO, The Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature were searched on February 12 and July 9, 2019, to collect relevant studies. A Google Scholar search, interpersonal network recommendations, and reference chaining were also conducted. Eligible studies included qualitative or mixed-methods studies focusing on the information needs (across the cancer continuum) of breast cancer patients or their social networks. Subsequently, a Critical Appraisals Skills Programme checklist was used to assess the quality of included research. The results, findings, and discussions were extracted. Data analysis was guided by the theory-generating meta-synthesis and grounded theory approach. ResultsThree themes, 19 categories, and 55 concepts emerged: (1) incentives (physical abnormality, inquiry from others, subjective norm, and problems during appointments); (2) types of information needs (prevention, etiology, diagnosis, clinical manifestation, treatment, prognosis, impact and resumption of normal life, scientific research, and social assistance); (3) moderating variables (attitudes, health literacy, demographic characteristics, disease status, as well as political and cultural environment). The studies revealed that the information needs of breast cancer patients were triggered by different incentives. Subsequently, the patients sought a variety of information among different stages of the cancer journey. Five types of variables were also found to moderate the formation of information needs. ConclusionsThis study contributes to a thorough model of information needs among breast cancer patients and provides practical suggestions for health and information professionals.

To investigate the differences in volatile components during the processing of Fuling Zhacai, or pickled mustard tuber, with three stacked layers of mustard tuber and salt, gas chromatography-tandem mass spectrometry (GC-MS/MS)-based non-targeted metabolomics combined with multivariate statistical analysis was used to analyze the changes in volatile metabolites and significantly differential metabolic pathways during the spontaneous fermentation of Zhacai. The results showed that a total of 627 volatile compounds were identified from Zhacai samples at different pickling stages, including terpenoids, esters, heterocyclic compounds, terpenoids, aldehydes, ketones, alcohols, phenols, amines, sulfur compounds, acids, halogenated hydrocarbons, and nitrogen compounds. A significant difference was observed in volatile components among pickling stages. A total 157 unique differential metabolites in the 1st pickling stage vs. the control group, 28 in the 2nd vs. the 1st pickling stage, and one in the 3rd vs. 2nd pickling stage were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the differential metabolites were significantly enriched in metabolic pathways including secondary metabolite biosynthesis, diterpenoid biosynthesis, monoterpenoid biosynthesis, caffeine metabolism, and fatty acid biosynthesis. This study provides a theoretical basis for the standardized production of Fuling Zhacai.

Purposes For improving the mechanical properties of GO incorporated cement paste, two types of nano-silica (crystalline and amorphous) and coal-bearing metakaolin (CMK) were used seprately to improve GO dispersion in cement paste. Methods UV-vis spectrophotometry was used to investigate the effects of ultrasound time and different dosages of amorphous nano-silica (ANS) and crystalline nano-silica (CNS) on the dispersion of GO in saturated calcium hydroxide solution. The effects of different particle materials on GO dispersion were characterized by using compressive strength results. The dehydration and decomposition processes of various phases of hydration products were observed by thermogravimetric (TG) analysis, and the effect of adding different particle materials and GO on the pore structure of cement paste was analyzed by mercury intrusion porosimetry (MIP). Findings According to UV-vis spectroscopic study, when the contents of ANS and CNS were 2% and 1.5% by weight of cement, respectively, GO dispersion in saturated Ca(OH)2 solution is improved greatly. Compared with two nano-silicas, CMK is more effective to improve the compressive strength of GO incorporated cement paste. At the age of 28 d, the flexural strength and compressive strength of the cement paste incorporated with 4% CMK and 0.03% GO are 26.93% and 30.41%, respectively, higher than those of a plain cement paste sample. The results of TG and MIP tests showed that the pozzolanic reaction of CMK can effectively refine the pore structure of cement paste, improve GO dispersion, and enhance the bonding between hydration products and GO, which is the main reason why CMK can effectively improve the mechanical properties of GO incorporated cement paste.