OBJECTIVE: To identify impacts of different survey methodologies assessing primary care physicians\' (PCPs\') experiences with electronic health records (EHRs), we compared three surveys: the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine, the 2022 University of California San Francisco (UCSF) Physician Health IT Survey, and the 2021 National Electronic Health Records Survey (NEHRS).
METHODS: We evaluated differences between survey pairs using Rao-Scott corrected chi-square tests, which account for weighting.
RESULTS: CCQ received 3991 responses from PCPs (100% response rate), UCSF received 1375 (3.6% response rate), and NEHRS received 858 (18.2% response rate). Substantial, statistically significant differences in demographics were detected across the surveys. CCQ respondents were younger and more likely to work in a health system; NEHRS respondents were more likely to work in private practice; and UCSF respondents disproportionately practiced in larger academic settings. Many EHR experience indicators were similar between CCQ and NEHRS, but CCQ respondents reported higher documentation burden.
CONCLUSIONS: The UCSF approach is unlikely to supply reliable data. Significant demographic differences between CCQ and NEHRS raise response bias concerns, and while there were similarities in some reported EHR experiences, there were important, significant differences.
CONCLUSIONS: Federal EHR policy monitoring and maintenance require reliable data. This test of existing and alternative sources suggest that diversified data sources are necessary to understand physicians\' experiences with EHRs and interoperability. Comprehensive surveys administered by specialty boards have the potential to contribute to these efforts, since they are likely to be free of response bias.

BACKGROUND: Ensuring the emotional wellbeing of participants in violence-focused research is a paramount ethical requirement. While previous research suggests that most participants in violence-focused studies do not report harmful consequences, little is known about the experiences of adolescent participants in low- and middle-income countries.
OBJECTIVE: This study, conducted in Maharashtra, India, aims at assessing how participant, contextual, and interviewer characteristics affect the level of distress that adolescent girls experience after participation in a violence survey.
METHODS: A total of 3049 13-18-year-old girls were interviewed on their experiences of family and intimate partner violence. Following the interview, both girls and interviewers completed a 5-item questionnaire on perceived participant distress. Linear regression analyses were conducted to identify possible correlates of girls\' distress.
RESULTS: Less than 10 % of participants reported feelings of distress upon completion of the interview. Higher levels of interviewers\' empathy were significantly associated with decreased levels of participants\' distress (standardised beta: -0.25, p < 0.001). Reported distress was also lower if girls had opted for an audio- and mobile-assisted self-interview (ACASI) format (standardised beta: -0.05, p < 0.01) and if the interview was conducted by someone older (standardised beta: -0.22, p < 0.001). Conversely, if interviews were conducted in participants\' homes and by interviewers with higher education levels, reported distress was significantly higher (standardised beta: 0.06, p < 0.01 and 0.12, p < 0.001, respectively).
CONCLUSIONS: Our findings suggest that incorporating empathetic interviewing and trust-building techniques into interviewer training, offering ACASI interviews, and choosing interview locations that ensure confidentiality can help protect the wellbeing of participants in violence research.

Extensive research has focused on the impact of weather on working capacity and income. However, in regions where income data largely relies on surveys, a pivotal yet underexplored question is whether weather not only influence real income but also introduce biases into survey-collected income data. We analyze longitudinal data from the China Health and Nutrition Survey and corresponding weather records from the Global Surface Summary of the Day, and uncover a negative correlation between survey-day temperature and self-reported annual income from the previous year. With a series of robustness checks, we confirm that the effect is primarily driven by behavioral factors rather than actual income changes. And threshold regression analyses show that the impact of temperature is more pronounced on hot days and relatively subdued or even reversed on cooler days. Further analyses indicate that mood, rather than cognitive capacity, plays a central role in causing the observed downward bias.

Survey data on migration aspirations, plans and intentions is important for understanding the drivers and dynamics of migration. Such data has been collected since the 1960s but has expanded massively in recent decades. This paper provides the first comprehensive overview of existing survey data in an inventory of 212 surveys with recorded metadata on geographic and temporal coverage, survey population, sample size, and other characteristics. \'A survey\' is not always a clear-cut unit of analysis, but we adopted procedures that enable systematic comparisons, and identified surveys through systematic searches and follow-up investigation. The paper has three objectives. First, it facilitates reuse of survey data and secondary analysis, albeit with limitations in data access, which we document. Second, it helps consolidate a sprawling field and thereby contribute to methodological and theoretical strengthening. Third, it informs debates on the ethics, politics and biases of data collection by documenting broad patterns in the body of knowledge. The inventory of survey data on migration aspirations and related concepts gives migration researchers a new tool for locating existing data and strengthening the foundations for collecting new data.

Detecting participants not exerting sufficient effort (e.g., answering randomly; termed \'insufficient effort responding\' or IER) in self-report surveys is crucial but intricate. Challenges of appropriately removing IER resemble a surgeon excising necrotic tissue without being inadequate, excessive, or incorrect. Current cutoff criteria for stopping the elimination of suspected IER responses are often arbitrary. This study proposes an external criterion strategy to identify the optimal cutoff values for various IER detection methods. We investigated the change in correlations between the IER-containing scale (e.g., motivation) and the external criterion (e.g., academic performance, socioeconomic status index, or another questionnaire scale) utilizing simulated and authentic international survey data. The findings revealed that the stopping rule and consequently, the optimal cutoff values for IER detection methods, can be accurately identified by locating the inflection point in the correlation plot with the external criterion. Practical strategies are recommended for applied researchers.

OBJECTIVE: The study aimed to develop and validate a new scale called Musa Kazim\'s Dental Anxiety Scale (MK-DAS) to measure dental anxiety in relation to infectious diseases.
METHODS: The study utilized a cross-sectional design and recruited participants from Faculty of Dentistry, Altinbas University. The sample included 289 participants who were seeking dental treatment. The Modified Dental Anxiety Scale (MDAS) was employed for the purpose of assessing levels of dental anxiety. In contrast, the MK-DAS, comprised a series of seven inquiries specifically targeting concerns regarding the treatment procedure and the fear of contagion. The data was analyzed using various statistical methods, including descriptive statistics, exploratory factor analysis, criterion validity, cluster analysis for cut-off points, and test-retest reliability.
RESULTS: The factor analysis revealed that MK-DAS had a two-factor structure. The first factor consisted of five items related to various aspects of the treatment process (α:0.837), while the second factor included two items related to the fear of infectious diseases (α:0.747). The scale showed good reliability, as indicated by high Cronbach\'s alpha coefficients for both factors. Strong positive correlations were found between MDAS and the first factor of MK-DAS (r = 0.857; p < 0.01), moderate positive correlations between MDAS and the second factor (r = 0.323; p < 0.01), and a strong positive correlation between MDAS and the overall of MK-DAS (r = 0.782; p < 0.01). Additionally, the cluster analysis yielded a cut-off score of 17 based on the k-means analysis. Moreover, test-retest reliability analyses indicated that dimension 1 (ICC: 0.904), dimension 2 (ICC: 0.840), and overall MK-DAS (ICC: 0.944) demonstrated high internal consistency.
CONCLUSIONS: The MK-DAS is an innovative and modern dental anxiety scale that has been proven to be reliable and valid, surpassing the comprehensiveness of the MDAS.

BACKGROUND: Web-based surveys increase access to study participation and improve opportunities to reach diverse populations. However, web-based surveys are vulnerable to data quality threats, including fraudulent entries from automated bots and duplicative submissions. Widely used proprietary tools to identify fraud offer little transparency about the methods used, effectiveness, or representativeness of resulting data sets. Robust, reproducible, and context-specific methods of accurately detecting fraudulent responses are needed to ensure integrity and maximize the value of web-based survey research.
OBJECTIVE: This study aims to describe a multilayered fraud detection system implemented in a large web-based survey about COVID-19 attitudes, beliefs, and behaviors; examine the agreement between this fraud detection system and a proprietary fraud detection system; and compare the resulting study samples from each of the 2 fraud detection methods.
METHODS: The PhillyCEAL Common Survey is a cross-sectional web-based survey that remotely enrolled residents ages 13 years and older to assess how the COVID-19 pandemic impacted individuals, neighborhoods, and communities in Philadelphia, Pennsylvania. Two fraud detection methods are described and compared: (1) a multilayer fraud detection strategy developed by the research team that combined automated validation of response data and real-time verification of study entries by study personnel and (2) the proprietary fraud detection system used by the Qualtrics (Qualtrics) survey platform. Descriptive statistics were computed for the full sample and for responses classified as valid by 2 different fraud detection methods, and classification tables were created to assess agreement between the methods. The impact of fraud detection methods on the distribution of vaccine confidence by racial or ethnic group was assessed.
RESULTS: Of 7950 completed surveys, our multilayer fraud detection system identified 3228 (40.60%) cases as valid, while the Qualtrics fraud detection system identified 4389 (55.21%) cases as valid. The 2 methods showed only \"fair\" or \"minimal\" agreement in their classifications (κ=0.25; 95% CI 0.23-0.27). The choice of fraud detection method impacted the distribution of vaccine confidence by racial or ethnic group.
CONCLUSIONS: The selection of a fraud detection method can affect the study\'s sample composition. The findings of this study, while not conclusive, suggest that a multilayered approach to fraud detection that includes conservative use of automated fraud detection and integration of human review of entries tailored to the study\'s specific context and its participants may be warranted for future survey research.

To understand the dynamics that limit use of risk-management options by women at high risk of breast cancer, there is a critical need for research that focuses on patient perspectives. Prior research has left important gaps: exclusion of high-risk women not in risk-related clinical care, exclusion of non-white populations, and lack of attention to the decision-making processes that underlie risk-management choices. Our objective was to create a more inclusive dataset to facilitate research to address disparities related to decision making for breast cancer risk management.
The Daughter Sister Mother Project survey collects comprehensive information about the experiences of women at high risk of breast cancer. We collected novel measures of feelings about and reactions to cancer screenings; knowledge, barriers, and facilitators of risk-management options; beliefs related to cancer risk and risk management; and involvement with loved ones who had cancer. Eligible individuals were non-Hispanic white and non-Hispanic Black adult women who self-identified as having high risk of breast cancer and had no personal history of cancer. Between October 2018 and August 2019, 1053 respondents completed the online survey. Of these, 717 were confirmed through risk prediction modeling to have a lifetime breast cancer risk of ≥ 20%. Sociodemographic characteristics of this sample were compared to those of nationally representative samples of the US population: the 2019 Health Information National Trends Survey and the Pew Research Center report: Jewish Americans in 2020.
The sample of 717 women at objectively high risk of breast cancer was largely (95%) recruited from non-clinical sources. Of these respondents, only 31% had seen a genetic counselor, 34% had had genetic testing specific to breast cancer risk, and 35% had seen at least one breast or cancer care specialist. The sample includes 35% Black respondents and 8% with Ashkenazi Jewish ancestry. Although encompassing a substantial range of ages, incomes, and education levels, respondents are overall somewhat younger, higher-income, and more educated than the US population as a whole.
The DSM dataset offers comprehensive data from a community-based, diverse sample of women at high risk of breast cancer. The dataset includes substantial proportions of Black and Ashkenazi Jewish women and women who are not already in clinical care related to their breast cancer risk. This sample will facilitate future studies of risk-management behaviors among women who are and are not receiving high-risk care, and of variations in risk-management experiences across race and ethnicity.

When and how to survey potential respondents is often determined by budgetary and external constraints, but choice of survey modality may have enormous implications for data quality. Different survey modalities may be differentially susceptible to measurement error attributable to interviewer assignment, known as interviewer effects. In this paper, we leverage highly similar surveys, one conducted face-to-face (FTF) and the other via phone, to examine variation in interviewer effects across survey modality and question type. We find that while there are no cross-modality differences for simple questions, interviewer effects are markedly higher for sensitive questions asked over the phone. These findings are likely explained by the enhanced ability of in-person interviewers to foster rapport and engagement with respondents. We conclude with a thought experiment that illustrates the potential implications for power calculations, namely, that using FTF data to inform phone surveys may substantially underestimate the necessary sample size for sensitive questions.

Many adolescent health surveys ask if respondents are male or female. Non-response may be due to fear of de-anonymisation or being a gender-nonconforming youth. The present study investigates the frequency of non-response and its potential reasons. To this end, data from 54,833 adolescents aged 11-18 from six countries, participating in the 2018 Health Behaviour in School-aged Children (HBSC) study, were analysed. Respondents were divided into three groups: (1) \"Responders\" who answered both questions on age and gender, (2) \"Age non-responders\" who did not answer the question on age, and (3) \"Gender non-responders\" who answered the question on age but not the one on gender. These groups were compared regarding their non-response to other questions and regarding their health. Overall, 98.0% were responders, 1.6% were age non-responders and 0.4% were gender non-responders. On average, age non-responders skipped more questions (4.2 out or 64) than gender non-responders (3.2) and responders (2.1). Gender non-responders reported more psychosomatic complaints, more frequent substance use and lower family support than responders. This study shows that age and gender non-responders differ in their response styles, suggesting different reasons for skipping the gender question. The health disparities found between the groups suggest that further research should use a more nuanced approach, informed by LGBT+ youth\'s insights, to measure sex assigned at birth and gender identity.