Data set from PLOS ONE Article Published Entitled: Western Lowland Gorillas Signal Selectively Using Odor

Context

This dataset was given by our professor as a lab work of machine learning.

Content

In the exam.xls file, the results of a computer science exam are recorded. The following parameters are used:

id = final digit of the student matriculation number group = name of the learning group sex = gender (m = male, f = female) quanti = number of solved exercises points = total score achieved from the exercises exam = total score achieved from the final written exam (* "Students must have to participate in the final written exam. If not, he/she will be considered as fail") passed = self-explanatory

Please solve the following tasks as far as possible with the IBM SPSS Modeler in a single stream/with python programming language.

TASK 1: (statistics) Determine the average, median, mode, and standard deviation of the points from the exercises.

TASK 2: (regression)

Check whether the points in the exam (y value) is dependent from the total score from the exercises (x value) by preparing the data graphically! Perform a linear regression! What are the parameters of the trend line? Determine (by hand) the correlation coefficient between the points (x value) in the exercises and in the points in the exam (y value). Interpret the result!

Acknowledgements

Basically, We used IBM SPSS Modeler for performing the task, as most of the students were not from the computer science background. But due to my self-interest, I also tried to solve the task with the python data science library.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The original SPSS dataset used and analyzed in our study.

IntroductionCervical cancer is a significant public health problem for women worldwide. It is the fourth most frequent cancer in women globally. While early detection of cancerous lesions through screening tests leads to a better prognosis and a better chance of being cured, the number of people who go for screening is still low, especially for groups that are marginalized, like immigrant women.ObjectiveThe purpose of this study was to identify cervical cancer screening practices and factors influencing screening status among Yemeni immigrant women living in the Klang Valley, Malaysia.MethodA cross-sectional study among 355 randomly selected respondents between the ages of 20 and 65 was conducted through an online survey. A questionnaire was sent directly to the participants via WhatsApp. The analysis was conducted using SPSS 25 with a significance level of 0.05. It included descriptive analysis, chi-square and multiple logistic regression.ResultsThe response rate was 59%, with the majority of the respondents being married and between the ages of 35 and 49. Screening was reported at 23.1% in the previous three years. The final model revealed that age group 50–65 years (AOR = 5.39, 95% CI: 1.53–18.93), insurance status (AOR 2.22, 95% CI = 1.15–4.3), knowledge (AOR = 6.67, 95% CI = 3.45–12.9), access to health care facilities (AOR = 4.64, 95% CI = 1.29–16.65), and perceived barriers (AOR = 2.5, 95% CI = 1.3–4.83) were significant predictors of cervical screening uptake among Yemeni immigrant women in Malaysia (p<0.05).ConclusionAccording to the results, cervical cancer screening was found to be low among Yemeni immigrant women. The predictors were age group 50–65 years, insurance status, knowledge, access to health care facilities and perceived barriers. Efforts to enhance immigrant women’s participation in cervical cancer screening must tackle barriers to access to healthcare services as well as expand cervical cancer screening education programs.

BackgroundCurrently, the biggest issue facing the entire world is mental health. According to the Ethiopian Ministry of Health, nearly one-fourth of the community is experiencing any of the mental illness categories. Most of the cases were treated in religious and traditional institutions, which the community most liked to be treated. However, there were very limited studies conducted to show the level of mental health literacy among traditional healers.AimsThe study aimed to assess the level of mental health literacy and its associated factors among traditional healers toward mental illness found in Northeast, Ethiopia from September 1-30/2022.MethodA mixed approach cross-sectional study design was carried out on September 130, 2022, using simple random sampling with a total sample of 343. Pretested, structured questionnaires and face-to-face interviews were utilized for data collection. The level of Mental Health Literacy (MHL) was assessed using the 35 mental health literacy (35-MHLQ) scale. The semi-structured checklist was used for the in-depth interview and the FGD for the qualitative part. Data was entered using Epi-data version 4.6 and, then exported to SPSS version 26 for analysis. The association between outcome and independent variables was analyzed with bivariate and multivariable linear regression. P-values < 0.05 were considered statistically significant. Thematic analysis was used to analyze the qualitative data, and the findings were then referenced with the findings of the quantitative data.ResultsThe findings of this study showed that the sample of traditional healers found in Dessie City scored a total mean of mental health literacy of 91.81 ± 10:53. Age (β = -0.215, 95% CI (-0.233, -0.05), p = 0.003, informal educational status (β = -5.378, 95% CI (-6.505, -0.350), p = 0.029, presence of relative with a mental disorder (β = 6.030, 95% CI (0.073, 7.428),p = 0.046, getting information on mental illness (β = 6.565, 95% CI (3.432, 8.680), p =

These teaching datasets, comprising a sub-set of a large-scale longitudinal study, the Longitudinal Study of Young People in England (LSYPE), were created as part of the NCRM Developing Statistical Modelling in the Social Sciences: Lancaster-Warwick-Stirling Node Phase 2 project, funded by the Economic and Social Research Council (ESRC). During the project, a web site was created with the aim to provide a web-based training resource about the use of statistical regression methods in educational research. The content is designed to teach users how to perform a variety of regression analyses using SPSS, starting with foundation material in basic statistics and working through to more complex multiple linear, logistic and ordinal regression models. Along with illustrated modules the site contains demonstration videos, interactive quizzes and SPSS exercises and examples that use these LSYPE teaching data. Further information and documentation may be found at the web site, Using Statistical Methods in Education Research. Throughout the site modules users are invited to use the datasets for either following the examples or performing exercises. Prospective users of the data will be directed to register an account in order to download the data.

The full LSYPE study is held at the Archive under SN 5545. The teaching datasets include information drawn from Wave 1 of LSYPE, conducted in 2004, with GCSE results matched from Wave 3 (2006). Further information about the NCRM Node project covering this study may be found on the Developing Statistical Modelling in the Social Sciences ESRC award web page.

Documentation
There is currently no discrete documentation currently available with these teaching datasets; users should consult the web site noted above. Documentation covering the main LSYPE study is available with SN 5545.

For the second edition (July 2011), updated versions of the SPSS data files were deposited to resolve minor anomalies.

BackgroundThe achievement towards 100% exclusive breastfeeding still a challenge in many countries despite adverse impacts due to the absence of exclusive breastfeeding. One consequence from the low practice of exclusive breastfeeding is malnutrition, including stunting that can be prevented by providing optimal food to infants, starting with providing exclusive breastfeeding from birth to 6 months of age. However, the practice of exclusive breastfeeding still low and it is suspected that this practice also decreased during the COVID- 19 pandemic. This study aims to analyze the determinants of exclusive breastfeeding in sub-urban areas during the COVID-19 pandemic.MethodsThis study using cross sectional design conducted from interviewing 206 mothers in 2022 who meet the inclusion criteria, consisted of breastfeeding their babies in the last 1 year and live in Sub-urban area in Depok City, West Java. Multiple binary logistic regression used to measure the association and strength between independent variables with the outcome variable. Independent variables with a p-value < 0.25 during the Chi-square test were included in the logistic regression model.ResultsPrevalence of exclusive breastfeeding and early initiation of breastfeeding (EIB) was 58.3% and 57.8% respectively. Factors associated with exclusive breastfeeding practices are education, employment status, knowledge and attitude about exclusive breastfeeding, self-efficacy in providing exclusive breastfeeding, EIB practice, and eating pattern. From multivariate analysis, it was found that the dominant factors to exclusive breastfeeding are EIB.ConclusionsThe study highlights the importance of improving exclusive breastfeeding practice through early initiation of breastfeeding, mother’s knowledge, education and self-efficacy. Therefore, health promotion and education should emphasize the importance of those factors, supported by the health policy and massive campaign as a key success in exclusive breastfeeding.

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the Publisher's Website. Data Generation The research project engages a story about perceptions of fairness in criminal justice decisions. The specific focus involves a debate between ProPublica, a news organization, and Northpointe, the owner of a popular risk tool called COMPAS. ProPublica wrote that COMPAS was racist against blacks, while Northpointe posted online a reply rejecting such a finding. These two documents were the obvious foci of the qualitative analysis because of the further media attention they attracted, the confusion their competing conclusions caused readers, and the power both companies wield in public circles. There were no barriers to retrieval as both documents have been publicly available on their corporate websites. This public access was one of the motivators for choosing them as it meant that they were also easily attainable by the general public, thus extending the documents’ reach and impact. Additional materials from ProPublica relating to the main debate were also freely downloadable from its website and a third party, open source platform. Access to secondary source materials comprising additional writings from Northpointe representatives that could assist in understanding Northpointe’s main document, though, was more limited. Because of a claim of trade secrets on its tool and the underlying algorithm, it was more difficult to reach Northpointe’s other reports. Nonetheless, largely because its clients are governmental bodies with transparency and accountability obligations, some of Northpointe-associated reports were retrievable from third parties who had obtained them, largely through Freedom of Information Act queries. Together, the primary and (retrievable) secondary sources allowed for a triangulation of themes, arguments, and conclusions. The quantitative component uses a dataset of over 7,000 individuals with information that was collected and compiled by ProPublica and made available to the public on github. ProPublica’s gathering the data directly from criminal justice officials via Freedom of Information Act requests rendered the dataset in the public domain, and thus no confidentiality issues are present. The dataset was loaded into SPSS v. 25 for data analysis. Data Analysis The qualitative enquiry used critical discourse analysis, which investigates ways in which parties in their communications attempt to create, legitimate, rationalize, and control mutual understandings of important issues. Each of the two main discourse documents was parsed on its own merit. Yet the project was also intertextual in studying how the discourses correspond with each other and to other relevant writings by the same authors. Several more specific types of discursive strategies were of interest in attracting further critical examination: Testing claims and rationalizations that appear to serve the speaker’s self-interest Examining conclusions and determining whether sufficient evidence supported them Revealing contradictions and/or inconsistencies within the same text and intertextually Assessing strategies underlying justifications and rationalizations used to promote a party’s assertions and arguments Noticing strategic deployment of lexical phrasings, syntax, and rhetoric Judging sincerity of voice and the objective consideration of alternative perspectives Of equal importance in a critical discourse analysis is consideration of what is not addressed, that is to uncover facts and/or topics missing from the communication. For this project, this included parsing issues that were either briefly mentioned and then neglected, asserted yet the significance left unstated, or not suggested at all. This task required understanding common practices in the algorithmic data science literature. The paper could have been completed with just the critical discourse analysis. However, because one of the salient findings from it highlighted that the discourses overlooked numerous definitions of algorithmic fairness, the call to fill this gap seemed obvious. Then, the availability of the same dataset used by the parties in conflict, made this opportunity more appealing. Calculating additional algorithmic equity equations would not thereby be troubled by irregularities because of diverse sample sets. New variables were created as relevant to calculate algorithmic fairness equations. In addition to using various SPSS Analyze functions (e.g., regression, crosstabs, means), online statistical calculators were useful to compute z-test comparisons of proportions and t-test comparisons of means. Logic of Annotation Annotations were employed to fulfil a variety of functions, including supplementing the main text with context, observations, counter-points, analysis, and source attributions. These fall under a few categories. Space considerations. Critical discourse analysis offers a rich method...

Linear regression analysis of respondents in Dessie City, Northeast, Ethiopia, 2022 (N = 343).

This study aims to determine "the effect of conflict on employee performance at Giant Pekanbaru". In this study, a sample of 90 people was used. Data collection was carried out through questionnaires and data analysis techniques used with a significance level of 0.05 were validity test, reliability test with crobanchalpha, simple linear regression and t test analysis and analysis of determination R Square (R2). The results of the analysis and data of this study using the help of SPSS Version 16.0, the results of the simple linear regression equation are Y = 45.561 + 0.256X. Based on the results of the research on the t-test showed results, Tcount> Ttable or 2,250> 1,987. So it can be concluded that there is a significant influence between conflict on performance. Based on the data obtained from the variable Y (performance), obtained R Square (R2) of 0.597 or 59.7%. R Square is used to determine the percentage of the influence of the Independent variable (conflict) on the Dependent variable (performance) is 59.7% while the remaining 40.3% is influenced by other variables not examined.

Stepwise multiple regression analysis results with initial score as dependent variable.

This comprehensive research package documents the complete methodological and analytical materials from a discourse analysis study examining social discourse in Spanish digital media regarding biorefineries as renewable energy technologies (RETs). The research identifies narrative patterns, power dynamics, and communication strategies that influence the acceptance or rejection of biorefineries, providing empirical validation of the Communicative Hegemony Model in Energy Transition (CHMET).The study employs mixed qualitative and quantitative methodologies structured in four phases, analyzing 350 digital articles about 88 Spanish biorefineries published between November 2019 and November 2024. The qualitative approach involves systematic discourse analysis using social listening techniques and manual coding, while the quantitative dimension employs statistical tools including frequency analysis, chi-square tests, binary logistic regressions, and cluster analysis to establish significant relationships among key variables.This repository contains six main components: (1) the complete coded database with 350 news articles according to defined operational variables; (2) comprehensive SPSS 29 syntax files organized by analysis type (exploratory, logistic regression, cluster analysis, and multiple correspondence analysis); (3) complete statistical outputs in .spv format; (4) the database of 88 studied biorefineries with technical and geographical characteristics; (5) complete Social Onclusive monitoring configuration files; and (6) specific query sets designed for each monitored facility with detailed location and technology combinations.The coding framework follows the structured methodology outlined in Gertrudix et al. (2024) "Codebook for the analysis of social acceptance of biorefineries in Spain, based on public discourse in social media and digital media." The dataset comprises 49 variables encompassing geographical, organizational, and communication-related data, with detailed analyses of thematic, emotional, and argumentative dimensions.This resource supports researchers and practitioners exploring public perceptions of renewable energy technologies, democratic participation in energy transitions, and critical approaches to environmental communication, enabling replication and extension of the analytical framework to other contexts.

The purpose of the present study was to examine how information source (control—no source, USDA, fictitious hospital, or fictitious social media) impacts perceptions of diet information. Participants included 943 American adults who were aged 18-74 years (M = 37.51, SD = 9.50) and were recruited from across the United States through Amazon Mechanical Turk (MTurk). As a manipulation check we assessed whether participants accurately completed the manipulation by ensuring their response to the question of who made the flyer. Participants who answered the question incorrectly were excluded from the analysis. In total, 537 answered correctly and were included in the analyses (Control = 113, Hospital = 144, Social Media = 121, USDA = 159). The majority of our eligible sample identified as men (N = 350), while the remainder identified as women (N = 185), nonbinary (N = 1), or “other” (N = 1).Participants completed an online survey in which they viewed one flyer containing dietary information and guidance on consuming pulses. The purported source of the flyer information was manipulated to create the 4 conditions. Participants rated the flyer in terms of perceived accuracy, trustworthiness, reliability, desirability for learning more from the source, and likelihood of following the advice. Attitudes, perceived control and norms, and past behavior were used to measure components of the Theory of Planned Behavior (TPB). ANOVA results indicated that the USDA and hospital sources were perceived as more accurate, trustworthy, reliable, and more desirable to learn more from relative to control and social media. There were no differences in likelihood of following guidance depending on source. Multiple regression showed that measures of the TPB were predictors of likelihood of following advice. Participants also ranked their top 3 most trusted sources for health information from a list of 29 sources. Doctors, scientists, nurses, and family and friends were among the most frequently trusted sources. Overall, these findings suggest that trust in the source of information does not influence perceived likelihood of following dietary recommendations for pulses. Resources in this dataset:Resource Title: Effect of Source on Trust of Pulse Nutrition Information and Perceived Likelihood of Following Dietary Guidance. File Name: EffectofSource_Data.xlsxResource Description: One-way analyses of variance (ANOVA) were used to assess between-condition differences for ratings of each of the 5 primary dependent variables (i.e., perceptions of the flyer; variables named Flyer_InfoAccuracy, Flyer_TrustInSource, Flyer_SourceReliability, Flyer_LearnMore, Flyer_FollowAdvice). Tukey tests were used to examine all pairwise comparisons for each of the significant ANOVA effects. A bivariate Pearson correlation was used to examine the relationship between trust in source and likelihood of following advice (variables Flyer_TrustInSource and Flyer_FollowAdvice). Multiple regression/correlation (MRC) was used to assess whether components of the TPB (TPB_Attitudes1, TPB_Attitudes2, TPB_PerceivedNorms1, TPB_PerceivedNorms2, TPB_PerceivedControl1, TPB_PerceivedControl2, TPB_PastBehavior) were predictive of likelihood of following advice (Flyer_FollowAdvice). Finally, frequency data was used to assess percentage with which participants selected sources as being in their top 3 most trusted (Trust_Ald_2_0_GROUP1-Trust_Ald_2_0_29_RANK). Sources that were selected are noted as either 1, 2, or 3 depending on rank, and the sources participants did not select are listed as #NULL!. Data was analyzed using SPSS statistical software, version 28. Resource Software Recommended: SPSS,url: https://www.ibm.com/products/spss-statistics?utm_content=SRCWW&p1=Search&p4=43700050715561164&p5=e&gclid=EAIaIQobChMI2fnV4I6e-AIVErfICh00pwcfEAAYASAAEgIkHfD_BwE&gclsrc=aw.ds

The data is from the article "Privacy Signals: Exploring the Relationship Between Cookies and Online Purchase Intention" and shows consumer perceptions of website transparency about cookie requests in the e-commerce environment. Overall, we used a quantitative methodology, through a descriptive study and four experimental studies. The results studies show that cookie acceptance positively influences the intention to purchase, only when the consumer accepts cookie collection and when they have a need for the product, resulting in greater perception of benefits associated with information disclosure. Risks did not show significance in this process. However, providing more information to consumers about data collection is advantageous because the intention to purchase is higher, even for those who do not accept cookies.

The data for the experiments were collected using Qualtrics software and online, using Facebook, through sponsored ads to ensure the randomness of responses. The products used in the experiment scenarios were chosen because of the pandemic context, where the consumption of products used in the home increased, to the detriment of superfluous or luxury products. The samples of the experiments respected the minimum criteria of 30 people in each experiment condition, as suggested by Hair et al. (2009). Statistical analyses were done through IBM SPSS Statistics statistical software using the Procces macro, which is an extension created for SPSS for multivariate data analysis and mediation analysis, as well as integrated conditional process models (Hayes, 2018).

Finally, we also used General Linear Model (GLM) in the analyses, as it is an extension of the linear regression model and indicated in cases of probability distributions other than the normal distribution, which makes it more flexible to handle the data (Hair et al. 2009).

Social Influence on Shopping

Social Survey Data from 300,000 Millennials and Gen Z Members

By Adam Halper [source]

About this dataset

This dataset offers a comprehensive look into the shopping habits of millennials and Gen Z members, including valuable insights about how their choices are influenced by social media. By exploring the responses given to survey questions related to this topic, we can gain an understanding of how these generations' interests, beliefs and desires shape their decisions when it comes to retail experiences. With 150 million survey responses from our 300,000+ millennial and Gen Z participants, we can uncover powerful insights that could help influencers, businesses and marketers more accurately target this demographic. Our data includes important information such as questions asked during the survey, segment types targeted by those questions and corresponding answers gathered with detailed counts/percentages - making this dataset incredibly useful for anyone wanting an in-depth understanding of what drives the purchasing behavior of today's youth

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For more datasets, click here.

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• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The first step in using this dataset is to take a look at each column: Question, Segment Type, Segment Description, Answer, Count & Percentage. The Question column will provide background on what exactly each survey question was asking - allowing you to get an overall view of what kind of topics were being surveyed in relation to millennials' shopping habits & social media influence. You will then be able to follow up with analysis based on the respective Segment Types & Descriptions given (such as income levels), which leads us into analyzing answers from both Count & Percentage columns combined - providing absolute numbers vs relative ones for further analysis (such as percentages).

Afterwards you'll need an advanced data analysis program such as SPSS or R-Studio - depending on your technical ability - though all most basic spreadsheet programs should suffice, excluding Matlab supported ones due its excessive complexity for something simple like this.. After selecting your preferred program inputting our file with all 150 million survey responses may take some time based on your computers processing capabilities but once loaded you'll be ready for endless possibilities! Now it's time get running with pulling out key insights you require utilizing various different tools found within these platforms whether it be linear regression or guided ANOVA testing which ever technique fits best should help lead navigate through uncovering deeper meaning in your ultra specific question!

As a final precaution while diving through waters filled surprises also keep note any adjustments needed potentially due overfitting or multicollinearity otherwise could cause major issues skew end results unfit requiring start whole process anew! Good luck delving deep discovering millennial behavior related digital world!

Research Ideas

• Identifying which type of segment is most responsive to engaging shopping experiences, such as influencer marketing, social media discounts and campaigns, etc.
• Analyzing the answers given to survey questions in order to understand millennial and Gen Z's opinion about social influence on their shopping habits - what do they view positively or negatively?
• Using the survey responses to uncover any interesting trends or correlations between different segments - is there a particular demographic that values or uses certain types of social influence on their shopping habits more than others?

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: WhatsgoodlyData-6.csv | Column name | Description ...

BackgroundInsufficient exercise affects the health of patients who have implantable cardioverter defibrillators (ICD). The purpose of this study was to investigate the correlations between exercise self-efficacy (ESE) and its associated psychological factors in ICD recipients.MethodsThis cross-sectional study included individuals who had undergone ICD implantation at the cardiology department of a medical centre in Taiwan. A face-to-face survey was conducted. The survey questionnaire included questions regarding the participants’ demographics, perceived health (PH), ICD shock–related anxiety (ICD-SRA), self-care self-efficacy (SSE), perceived exercise benefit (PE-benefit), perceived exercise barrier (PE-barrier), and ESE. Data were analysed using SPSS 20.0 Software. Stepwise multiple regression analyses were also performed to evaluate the predictive effects of the aforementioned factors on ESE.ResultsA total of 52 ICD recipients were enrolled. ESE was negatively correlated with ICD-SRA (r = −0.511; p < 0.01) and PE-barrier (r = −0.563; p < 0.01), but positively correlated with SSE (r = 0.339; p < 0.05) and PE-benefit (r = 0.464; p < 0.01). The stepwise multiple regression analysis revealed that PE-barrier, PE-benefit, and ICD-SRA effectively predicted ESE in the participants.ConclusionsESE may be improved by overcoming PE-barrier, ICD-SRA and enhancing PE-benefit. Consequently, improving ESE may enhance the health benefits of exercise.

This study aimed to develop training resources and standardize the assessment of dysphagia in patients with stroke. This study was a cross-sectional study. A total of 430 nurses and doctors from four provinces(Guangdong Province, Hunan Province, Guangxi Province, and Shaanxi Province) who were selected by convenience sampling were invited to complete the questionnaire through WeChat, DingTalk, and Tencent QQ from May 23 to 31, 2022. A self-reported questionnaire was used to assess participants’ Knowledge, Attitude, and Behavior regarding dysphagia. Participants’ sociodemographic, training, and nursing experience were measured using the general information sheet and assessed as potential predictors of medical staff’s Knowledge, Attitudes, and Behavior of dysphagia assessment. A multiple linear regression model was used to identify the factors predicting medical staff’s Knowledge, Attitudes, and Behavior regarding dysphagia assessment. The mean scores for Knowledge, Attitudes, and Behavior of dysphagia assessments were 92.654(SD 17.519). Multiple linear regression results indicated that experience in dysphagia patients’ nursing, related training for dysphagia, working years in the field of dysphagia-related diseases, specialized training in geriatric, swallowing & rehabilitation, and department related to neurology, rehabilitation & elderly were significant predictors, accounting for 35.1% of the variance in scores of medical staff’s Knowledge, Attitudes and Behavior of dysphagia assessment. Our findings imply that nursing experience, training, and work for patients with swallowing disorders could have positive effects on the Knowledge, Attitudes, and Behavior of medical staff regarding dysphagia assessment. Hospital administrators should provide relevant resources, such as videos of dysphagia assessment, training centers for the assessment of dysphagia, and swallowing specialist nurses. It is important that health policies fully recognize the role of training and support systems in caring for people with dysphagia. Methods Data Collection and Analysis This study was approved by the ethics committee of the study hospital. Data were collected on May 23–31, 2022. All participants were invited to complete the questionnaire through WeChat, DingTalk, and Tencent QQ from May 23 to May 31, 2022. A professional questionnaire survey platform that provides functions equivalent to Amazon Mechanical Turk called “Wenjuan Xing” was used to investigate. The researcher sent the questionnaire through WeChat, DingTalk, and Tencent QQ to colleagues and classmates to fill in and asked them to forward the questionnaire to their colleagues. Data were analyzed using SPSS for Windows, version 23. Statistical significance was set at p < 0.05. The findings were summarized using descriptive statistics, univariate analysis, and dummy multiple regression analysis. The data were normally distributed, as assessed by skewness and kurtosis tests. Descriptive statistics (e.g., mean, SD, or n, %) were used to summarize the study variables. Univariate analysis (independent-sample t-test and one-way analysis of variance for categorical independent variables) was performed to explore the potential predictors of medical staff KAB for dysphagia assessment. A comparison analysis was conducted to analyze the differences in medical staff’s KAB for dysphagia assessment among Guangdong Province, Hunan Province, Guangxi Province, and Shaanxi Province.

Stratum-specific multiple regression analysis of dietary nonadherence predictors based on the food security status of patients with T2DM at selected public hospitals in Addis Ababa, 2019.

Mnemonic-enhanced memory has been observed for negative events. Here, we investigate its association with spatiotemporal attention, consolidation, and age. An ingenious method to study visual attention for emotional stimuli is eye tracking. Twenty young adults and twenty-one older adults encoded stimuli depicting neutral faces, angry faces, and houses while eye movements were recorded. The encoding phase was followed by an immediate and delayed (48 h) recognition assessment. Linear mixed model analyses of recognition performance with group, emotion, and their interaction as fixed effects revealed increased performance for angry compared to neutral faces in the young adults group only. Furthermore, young adults showed enhanced memory for angry faces compared to older adults. This effect was associated with a shorter fixation duration for angry faces compared to neutral faces in the older adults group. Furthermore, the results revealed that total fixation duration was a strong predictor for face memory performance. Methods Participants Forty-one subjects participated in our study. They were recruited by advertisements for participation in an eye-tracker memory experiment. Participants did not receive financial compensation for their participation. Inclusion criteria consisted of (1) 18–30-year age range (young adults group) or 50–90-year age range (older adults group) and (2) an MMSE score above 25. The young adults and older adults group consisted of 20 participants [7 males (35%); mean age ± SD = 22 ± 2 years, range 18–29] and 21 participants [9 males (43%); mean age ± SD = 69 ± 7 years, range 53–87], respectively. One participant from the older adults group was not included in the eye movement analysis due to technical issues. Participants completed the Addenbrooke’s Cognitive Examination III (ACE-III), which includes the Mini–Mental State Examination (MMSE). All participants had an ACE-III score above 71. Eye Tracker and Eye Movement Recordings Eye movement data were collected during the encoding phase at a sampling rate of 120 Hz using the Tobii eye tracker TX300 and processed with Tobii Studio 3.4.7. During recording, the eye tracker collects raw eye movement data points, which are processed into fixations and used to calculate eye-tracking metrics, by applying a fixation filter to the data. We applied default settings, including the Tobii fixation filter, with a velocity threshold of 0.84 pixels/ms (35 pixels) and a distant threshold (distance between two consecutive fixations) of 35 pixels (default). In short, peak values are identified, i.e., the values that are greater than both of its two closest neighbors. The list of peaks is then processed into fixations, where the start and end points of a fixation are set by two consecutive peaks. The spatial positions of the fixations are calculated by taking the median of the unfiltered data points in that interval. Secondly, the Euclidean distances between all the fixations are calculated and if the distance between two consecutive fixations falls below a second user-defined threshold, the two fixations are merged into a single fixation. The process is repeated until no fixation points are closer to each other than the threshold. A detailed description of the Tobii fixation can be found in the Tobii Studio user manual (https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-studio-user-manual.pdf). Statistical Analysis Behavioral Analyses Behavioral results were analyzed according to signal detection theory. R-Score Plus was used to calculate d’ for confidence rating designs. D’ was calculated as a function of category (face vs. house), emotion (angry vs. neutral), interval (IR vs. DR), and group (older adults and young adults). We calculated the mean interval between the encoding phase and DR (lag) for every participant. To evaluate the anticipated outcomes for group differences in d’ in the IR phase, we performed the following general multivariate regression model, which takes repeated measures within subjects into account. Let Yi be a vector with repeated measures for the ith subject (i … N). This general multivariate regression model assumes that Yi satisfies the following regression model: Yi = Xiβ + εi with Xi being a matrix of covariates (e.g., intercept, group, emotion condition, and group x emotion condition), β is a vector of regression coefficients, and εi is a vector of error components with εi∼N(0, Σ). For the variance/covariance structure Σ of each subject, we considered a compound symmetry and unstructured variance/covariance matrix. Selection of the adequate variance/covariance matrix was based on a likelihood-ratio test. Reference coding was used for group (2 levels: older adults = 1 vs. young adults = 0) and emotion (2 levels: neutral = 1 vs. angry = 0). To evaluate main and interaction effects, Bonferroni-corrected post hoc tests were used. It may be noted that this model is a special case of a linear mixed model (Verbeke and Molenberghs, 2000) and that the mean structure Xiβ (the parameters of interest) can be interpreted as that in a classical ANOVA or regression model. Second, we performed a similar model but with category ((2 levels: neutral = 1 vs. angry = 0)) instead of emotion as predictor. These analyses were performed for the two different memory stages (IR and DR) separately. Lastly, we performed a similar model but with intervals (2 levels: IR = 1 vs. DR = 0) for the different conditions (house, face, angry face, neutral face) separately. Finally, a similar model was used with groups (2 levels: older adults = 1 vs. young adults = 0), intervals (2 levels: IR = 1 vs. DR = 0), and group x interval as predictors. All analyses were performed in SPSS. Eye-Tracker Analyses Eye movement data were calculated for house, face, and the three areas of interest: mouth, nose, and eyes. For every participant, two indices for eye movement data were recorded: total fixation duration and fixation count. Total fixation duration represents the total time of fixation as it measures the sum of the duration (seconds) for all fixations within an area of interest for all test stimuli throughout the experiment. Fixation count measures the number of fixations in each area of interest for all test stimuli throughout the experiment. If during the recording the participant leaves and returns to the same media element, this is counted as a new fixation. A detailed description of the metric measures can be found in the Tobii Studio user manual (https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-studio-user-manual.pdf). We exported the gaze data from Tobii Studio to SPSS for further analysis. Statistical tests on the gaze data were preceded by a normality check on the distributions of the respective residuals by means of a Shapiro–Wilk test. In case normality could not be assumed, non-parametric tests were performed (Mann–Whitney and Wilcoxon tests). In order to investigate the association between the behavioral data and eye movements, we performed Spearman correlations. We computed correlations between d’ (IR, DR) and eye tracker data (total fixation duration and fixation count) during encoding for both groups separately (young adults and older adults).

ObjectivesThis study aims to investigate patient safety culture in secondary hospitals of Heilongjiang, Northeast China, and explore the implications of patient safety culture and practices through the perspectives of various healthcare workers.MethodsA cross-sectional survey using the Safety Attitude Questionnaire (SAQ) was conducted to ascertain the status of patient safety culture in nine secondary hospitals across the six dimensions of the SAQ. Among the 900 staff members who were invited to participate, 665 completed the questionnaire. Descriptive statistics were used to calculate the general means and standard deviations of the patient safety culture dimensions and other numerical variables, and F-test and a multivariate regression analysis were used to statistically analyze the differences in perceptions of safety culture considering the differences in demographic characteristics. All statistical analyses were performed using SPSS v. 22.0.ResultsThe respondents rated job satisfaction as the highest among all six dimensions of the SAQ, followed in order by teamwork climate, working conditions, and stress recognition (the lowest). There were significant differences among the dimensions of patient safety culture and other factors, such as gender, age, job position, and education. Compared with previous studies, teamwork climate and working conditions scores were quite high, while stress recognition score was very low. We also found differences in patient safety culture by demographic characteristics.ConclusionsThe findings revealed the patient safety culture attitudes of healthcare workers in secondary hospitals of Heilongjiang, and provided baseline data for related future research. This evidence may also help government health policymakers and hospital administrators understand related challenges and develop strategies to improve patient safety culture in secondary hospitals of China and perhaps also in other developing countries.