This statistic displays the distribution of caregivers in the U.S. as of 2019, by the time it takes them to travel to the person they care for. It was found that 43 percent of caregivers need less than 15 minutes to travel to the person they care for.

IH157 - All persons aged 15 and over, time spent walking on a typical day. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).All persons aged 15 and over, time spent walking on a typical day...

Cameroon CM: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data was reported at 15.821 % in 2014. Cameroon CM: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data is updated yearly, averaging 15.821 % from Dec 2014 (Median) to 2014, with 1 observations. Cameroon CM: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Health Statistics. The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.; ; National statistical offices or national database and publications compiled by United Nations Statistics Division; ;

This data description contains code (written in the R programming language), as well as processed data and results presented in a research article (see references). No raw data are provided and the data that are made available cannot be linked to study participants. The sample consists of 180 of 308 eligible participants (adult primary care patients in Sweden, living with chronic illness) who responded to a Swedish web-based questionnaire at two time points. Using a confirmatory factor analysis, we calculated latent factor scores for 9 constructs, based on 34 questionnaire items. In this dataset, we share the latent factor scores and the latent profile analysis results. Although raw data are not shared, we provide the questionnaire item, including response scales. The code that was used to produce the latent factor scores and latent profile analysis results is also provided.

The study was performed as part of a research project exploring how the use of eHealth services in chronic care influence interaction and collaboration between patients and healthcare. The purpose of the study was to identify subgroups of primary care patients who are similar with respect to their experiences of co-care, as measured by the DoCCA scale (von Thiele Schwarz, 2021). Baseline data were collected after patients had been introduced to an eHealth service that aimed to support them in their self-care and digital communication with healthcare; follow-up data were collected 7 months later. All patients were treated at the same primary care center, located in the Stockholm Region in Sweden.

Cited reference: von Thiele Schwarz U, Roczniewska M, Pukk Härenstam K, Karlgren K, Hasson H, Menczel S, Wannheden C. The work of having a chronic condition: Development and psychometric evaluation of the Distribution of Co-Care Activities (DoCCA) Scale. BMC Health Services Research (2021) 21:480. doi: 10.1186/s12913-021-06455-8

The DATASET consists of two files: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

• factorscores_docca.csv: This file contains 18 variables (columns) and 180 cases (rows). The variables represent latent factors (measured at two time points, T1 and T2) and the values are latent factor scores. The questionnaire data that were used to produce the latent factor scores consist of 20 items that measure experiences of collaboration with healthcare, based on the DoCCA scale. These items were included in the latent profile analysis. Additionally, latent factor scores reflecting perceived self-efficacy in self-care (6 items), satisfaction with healthcare (2 items), self-rated health (2 items), and perceived impact of e-health (4 items) were calculated. These items were used to make comparisons between profiles resulting from the latent profile analysis. Variable definitions are provided in a separate file (see below).

• latent-profile-analysis-results_docca.csv: This file contains 14 variables (columns) and 180 cases (rows). The variables represent profile classifications (numbers and labels) and posterior classification probabilities for each of the identified profiles, 4 profiles at T1 and 5 profiles at T2. Transition probabilities (from T1 to T2 profiles) were not calculated due to lacking configural similarity of profiles at T1 and T2; hence no transition probabilities are provided.

The ASSOCIATED DOCUMENTATION consists of one file with variable definitions in English and Swedish, and four script files (written in the R programming language):

• variable-definitions_swe-eng.xlsx: This file consists of four sheets. Sheet 1 (scale-items_original_swedish) specifies the questionnaire items (in Swedish) that were used to calculate the latent factor scores; response scales are included. Sheet 2 (scale-items_translated_english) provides an English translation of the questionnaire items and response scales provided in Sheet 1. Sheet 3 (factorscores_docca) defines the variables in the factorscores_docca.csv dataset. Sheet 4 (latent-profile-analysis-results) defines the variables in the latent-profile-analysis-results_docca.csv dataset.

• R-script_Step-0_Factor-scores.R: R script file with the code that was used to calculate the latent factor scores. This script can only be run with access to the raw data file which is not publicly shared due to ethical constraints. Hence, the purpose of the script file is code transparency. Also, the script shows the model specification that was used in the confirmatory factor analysis (CFA). Missingness in data was accounted for by using Full Information Maximum Likelihood (FIML).

• R-script_Step-1_Latent-profile-analysis.R: R script file with the code that was used to run the latent profile analyses at T1 and T2 and produce profile plots. This code can be run with the provided dataset factorscores_docca.csv. Note that the script generates the results that are provided in the latent-profile-analysis-results_docca.csv dataset.

• R-script_Step-2_Non-parametric-tests.R: R script file with the code that was used to run non-parametric tests for comparing exogenous variables between profiles at T1 and T2. This script uses the following datasets: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

• R-script_Step-3_Class-transitions.R: R script file with the code that was used to create a sankey diagram for illustrating class transitions. This script uses the following dataset: latent-profile-analysis-results_docca.csv.

Software requirements: To run the code, the R software environment and R packages specified in the script files need to be installed (open source). The scripts were produced in R version 4.2.1.

This indicator is designed to accompany the SHMI data at site of treatment level. The SHMI is calculated at the level of the provider spell, which is a continuous period of time spent as a patient within a single trust (provider). A spell may be composed of more than 1 episode (a single period of care under 1 consultant). If a patient is moved between hospitals or sites within the same trust, the provider spell continues. Most spells consist of a single episode and so there is no complication when presenting SHMI data at site level because the entire provider spell occurred at a single site. However, spells consisting of multiple episodes may have occurred over multiple sites and only 1 of these sites can be associated with the spell. This has been chosen to be the site of the 1st episode in the spell. This may result in hospital deaths being attributed to a site other than the one in which they occurred, with an impact on the SHMI values presented for the sites concerned. This impact is likely to be greater for sites within trusts showing higher percentages for this contextual indicator. This indicator is being published as an experimental statistic. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells for England from March 2020 due to COVID-19 impacting on activity and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for this trust are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), East and North Hertfordshire NHS Trust (trust code RWH), Epsom and St Helier University Hospitals NHS Trust (trust code RVR), Frimley Health NHS Foundation Trust (trust code RDU), Imperial College Healthcare NHS Trust (trust code RYJ), Manchester University NHS Foundation Trust (trust code R0A), Norfolk and Norwich University Hospitals NHS Foundation Trust (trust code RM1), Sandwell and West Birmingham Hospitals NHS Trust (trust code RXK), and University Hospitals of Derby and Burton NHS Foundation Trust (trust code RTG) are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information is available in the Background Quality Report. 6. On 1 July 2023 Southport and Ormskirk Hospital NHS Trust (trust code RVY) was acquired by St Helens and Knowsley Teaching Hospitals NHS Trust (trust code RBN). The new organisation is known as Mersey and West Lancashire Teaching Hospitals NHS Trust (trust code RBN). However, data are still reported for the two separate organisations. NHS England are planning to report data for the new merged organisation from the next publication in October 2023. 7. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

Mexico MX: Time Spent Dealing with the Requirements of Government Regulations: % of Senior Management Time data was reported at 13.600 % in 2010. This records a decrease from the previous number of 20.500 % for 2006. Mexico MX: Time Spent Dealing with the Requirements of Government Regulations: % of Senior Management Time data is updated yearly, averaging 17.050 % from Dec 2006 (Median) to 2010, with 2 observations. The data reached an all-time high of 20.500 % in 2006 and a record low of 13.600 % in 2010. Mexico MX: Time Spent Dealing with the Requirements of Government Regulations: % of Senior Management Time data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Company Statistics. Time spent dealing with the requirements of government regulations is the proportion of senior management's time, in a typical week, that is spent dealing with the requirements imposed by government regulations (e.g., taxes, customs, labor regulations, licensing and registration, including dealings with officials, and completing forms).; ; World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/).; Unweighted average;

Belarus BY: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data was reported at 9.514 % in 2015. Belarus BY: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data is updated yearly, averaging 9.514 % from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 9.514 % in 2015 and a record low of 9.514 % in 2015. Belarus BY: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Health Statistics. The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.;National statistical offices or national database and publications compiled by United Nations Statistics Division. The data were downloaded on April 4, 2024, from the Global SDG API: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html;;This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/].

IntroductionPatient satisfaction with clinical services can have an effect on retention in HIV care and adherence to antiretroviral therapy. This study assessed patient satisfaction and its association with retention and viral suppression in Zambézia Province, Mozambique.MethodsMonthly exit interviews with persons living with HIV were completed from August 2017-January 2019 in 20 health facilities; clinical data were extracted from medical records. Regression analyses assessed the effect of satisfaction scores on retention and viral suppression, adjusting for age, sex, education, civil status, time on treatment, and site. Satisfaction scores were correlated with time spent at health facilities using generalized linear regression models.ResultsData from 4388 patients were analyzed. Overall median satisfaction score was 75% (IQR 53%-84%); median time spent at facilities (from arrival until completion of clinical services) was 2h54min (IQR 1h48min-4h). Overall satisfaction score was not associated with higher odds of retention or viral suppression, but association was seen between satisfaction regarding attention given to patient and respect and higher odds of viral suppression. Patient satisfaction was negatively associated with time spent in facility (Spearman’s correlation -0.63). Increased time spent at facility (from 1 to 3 hours) was not associated with lower retention in care (OR 0.72 [95%CI:0.52–1.01] and 0.83 [95%CI: 0.63–1.09] at 6- and 12-months, respectively), nor with a lower odds of viral suppression (OR 0.96 [95%CI: 0.71–1.32]).ConclusionsStrategies to reduce patient wait times at the health facility warrant continued prioritization. Differentiated models of care have helped considerably, but novel approaches are still needed to further decongest crowded health facilities. In addition, a good client-provider communication and positive attitude can improve patient satisfaction with health services, with an overall improved retention.

Average time spent being physically active, household population by sex and age group.

General social survey (GSS), average time spent at various locations for the population aged 15 years and over, by population cohorts.

Netherlands NL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data was reported at 14.720 % in 2012. This records a decrease from the previous number of 16.940 % for 2006. Netherlands NL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data is updated yearly, averaging 15.070 % from Dec 2001 (Median) to 2012, with 4 observations. The data reached an all-time high of 16.940 % in 2006 and a record low of 14.720 % in 2012. Netherlands NL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank: Health Statistics. The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.; ; National statistical offices or national database and publications compiled by United Nations Statistics Division; ;