Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The last census was carried out in Georgia in 1989. Because of various political and economical events in the country, such as conflict in Abkhazia and Tskhinvali region, civil war, etc., which caused migration, there are no population lists available that could be used for the sampling purposes. Lists prepared for elections are inaccurate. Based on the existing statistical data, a random sample design was used and a Random Walk Procedure was followed. This design was exceptionally accepted by WHO. A total of 10 regions were sampled and 10,000 were drawn from these regions: Region 1: Tbilisi Region 2: Ajara Region 3: Guria Region 4: Imereti Region 5: Kakheti Region 6: Mstkheta-Mtianeti Region 7: Samegrelo Region 8: Samtskhe-Javakheti Region 9: Kvemo Kartli Region 10: Shida Kartli The sampling frame covered urban and rural areas, however due to the political situation the Abkhazia and Tskhinvali regions were excluded. More females (57.8%) than males (42.2%) were interviewed.

Because of the questionnaire size and the difficult winter period of the fieldwork a higher non-response rate was anticipated. However, the total percentage of non-responses was much lower than expected. The main reasons of refusals to participate in interviews were mistrust, fear, and irritation due to their bad socioeconomic conditions. As well, interview duration was reported as being a problem. Further, in regions and sub regions of Georgia with a predominant non-Georgian population the language barrier became one additional negative factor, even if a bilingual questionnaire was used. In the Kvemo Kartli region, the Azeri population hardly understood either Georgian or Russian. Another problem was religion. Female Muslim respondents were not allowed to participate in the survey without the permission of their husbands who often were present during the interviews.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Characteristics of study participants, by study group.

Internet gaming addiction (IGA) has become a common phenomenon that affects adolescents, due to its possible negative effects on physical and mental health issues. However, very few studies have particularly examined the relationship between adolescent game addiction and parental influences. In this study, we address some undesirable parental behaviors and aim to explore whether they influence adolescents’ internet gaming behaviors. A total of 315 adolescents who have exposed to Internet games participated in this study. We examined the relationship between four parental factors and the development process examined by the structural equation modeling (SEM) techniques: adolescent Internet gaming addiction (IGA), parental interpersonal conflict (PIC), parental loneliness (PL), parental phubbing (PP), and parental rejection (PR). We proposed nine hypotheses, five of which were supported by the data. The results suggested that parental loneliness leads to parental phubbing and rejection behaviors, as well as enhancing Internet gaming addiction among adolescents. Additionally, parental interpersonal conflict can cause parental loneliness. However, the study found that parental loneliness, parental rejection, and parental interpersonal conflict do not statistically significant impact on adolescents’ internet gaming behaviors.

The coronavirus disease 2019 (COVID-19) pandemic has had a major influence on working patterns worldwide, given the various lockdown periods and the shift to remote working. As people’s noise perception is known to be closely linked with their work performance and job satisfaction, investigating the noise perception in indoor spaces, especially in situations where people work from home, is crucial; however, studies on this aspect are limited. Thus, here, this study aimed to investigate the relationship between indoor noise perception and remote work during the pandemic. The study assessed how people who worked from home perceived indoor noise, and how it related with their work performance and job satisfaction. A social survey was conducted with respondents who worked from home during the pandemic in South Korea. A total of 1,093 valid responses were used for data analysis. Structural equation modeling was used as a multivariate data analysis method to simultaneously estimate multiple and interrelated relationships. The results showed that indoor noise disturbance significantly affected annoyance and work performance. Annoyance with indoor noise affected job satisfaction. Job satisfaction was found to have a significant impact on work performance, particularly on two dimensions of the work performance that are crucial for achieving organizations’ goals. Moreover, one dimension of the work performance had a significant impact on annoyance. The study proposed that reducing negative perception of indoor noise and improvement of job satisfaction can lead to the maximization of one’s work performance when working from home.

ABSTRACT Purpose: To translate and validate a questionnaire that evaluates the theoretical knowledge regarding fundus examination. Methods: A 60-item multiple-choice English questionnaire that investigates various aspects of knowledge regarding fundus examination was translated into Portuguese. The process involved translation, back-translation, and evaluation by an expert committee. The resulting questionnaire was applied to final-year medical students and ophthalmology residents. Each included subject answered the questionnaire twice, with an interval of one week between each application. Internal consistency, test-retest reliability, inter-rater reliability, and percentage agreement were calculated. Results: Thirty participants were included (25 medical students and 5 ophthalmology residents). The pass-fail cutoff was calculated at 46, the theoretical false positives were 8.7% and the theoretical false negatives were 2.8%. The observed false positive and false negative rates were 0%. Among the 60 items, test-retest reliability was strong in 17 items, which one had a negative correlation, moderate in 14 items, which one had a negative correlation, and weak in 29 items; inter-rater reliability of 34 items was under 0.4, 17 items were between 0.4 and 0.6, and 8 items were above 0.6. One item had a negative kappa. Among the percent agreement, 10 items were between 40%-60% agreement, 50 were above 60% agreement, and 18 were above 80%. Cronbach’s alpha was calculated as 0.674. Conclusions: The translated questionnaire provided a standard instrument for future research and interventions to improve medical education in ophthalmology.