48 datasets found
  1. Number of minutes U.S. dermatologists spend with each patient 2018

    • statista.com
    Updated Nov 30, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2023). Number of minutes U.S. dermatologists spend with each patient 2018 [Dataset]. https://www.statista.com/statistics/664165/dermatologist-minutes-with-patients-us-by-gender/
    Explore at:
    Dataset updated
    Nov 30, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 21, 2017 - Feb 21, 2018
    Area covered
    United States
    Description

    This statistic shows the number of minutes that dermatologists in the U.S. spend with each patient as of 2018. It was found that 42 percent of dermatologists spend an average of between 9 to 12 minutes with each patient.

  2. Patient opinion on time spent on hospital waiting list in NHS England 2023

    • statista.com
    Updated Jun 23, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Patient opinion on time spent on hospital waiting list in NHS England 2023 [Dataset]. https://www.statista.com/statistics/1021670/attitudes-towards-hospital-waiting-list-england/
    Explore at:
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2024 - Apr 2024
    Area covered
    England
    Description

    In 2023, a survey of patients in England asked about their opinions regarding the waiting time they experienced before being admitted to the hospital. According to the results, ** percent of patients did not mind waiting as long as they did, although ** percent would like to have been admitted a lot sooner. This statistic shows the patient's opinion towards time spent on the hospital waiting list in England in 2023.

  3. AI reducing time burden of admin tasks to healthcare professionals in Europe...

    • statista.com
    Updated Jun 25, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). AI reducing time burden of admin tasks to healthcare professionals in Europe 2020 [Dataset]. https://www.statista.com/statistics/1202254/time-ai-could-save-in-healthcare-administration-europe/
    Explore at:
    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Europe
    Description

    In 2020, in Europe, it was estimated that a physician's working time is roughly split 50-50 between treating patients and administrative tasks. However, it has been forecast with the implementation of AI technologies in healthcare, physicians would be able to spend ** percent more of their time on patients because the time burden of administrative tasks would be reduced.

  4. Association between time spent in emergency care and 30-day post-discharge...

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Jan 17, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Office for National Statistics (2025). Association between time spent in emergency care and 30-day post-discharge mortality, England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthcaresystem/datasets/associationbetweentimespentinemergencycareand30daypostdischargemortalityengland
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jan 17, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Relationship between total time spent in an accident and emergency (A&E) department and the risk of 30-day, post-discharge, all-cause mortality, controlling for other factors. March 2021 to April 2022.

  5. n

    Data from: What keeps family physicians busy in Portugal? A multi-centre...

    • data.niaid.nih.gov
    • dataone.org
    • +1more
    zip
    Updated Jun 19, 2014
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Mónica Granja; Carla Ponte; Luís Felipe Cavadas (2014). What keeps family physicians busy in Portugal? A multi-centre observational study of work other than direct patient contacts [Dataset]. http://doi.org/10.5061/dryad.2hr40
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 19, 2014
    Dataset provided by
    Porta do Sol Family Health Unit, Matosinhos Local Health Unit, Matosinhos, Portugal
    S. Mamede de Infesta Health Centre, Matosinhos Local Health Unit, Matosinhos, Portugal
    Lagoa Family Health Unit, Matosinhos Local Health Unit, Matosinhos, Portugal
    Authors
    Mónica Granja; Carla Ponte; Luís Felipe Cavadas
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    Portugal
    Description

    Objectives: To quantify the time spent by family physicians (FP) on tasks other than direct patient contact, to evaluate job satisfaction, to analyse the association between time spent on tasks and physician characteristics, the association between the number of tasks performed and physician characteristics and the association between time spent on tasks and job satisfaction. Design: Cross-sectional, using time-and-motion techniques. Two workdays were documented by direct observation. A significance level of 0.05 was adopted. Setting: Multicentric in 104 Portuguese family practices. Participants: A convenience sample of FP, with lists of over 1000 patients, teaching senior medical students and first-year family medicine residents in 2012, was obtained. Of the 217 FP invited to participate, 155 completed the study. Main outcomes measured: Time spent on tasks other than direct patient contact and on the performance of more than one task simultaneously, the number of direct patient contacts in the office, the number of indirect patient contacts, job satisfaction, demographic and professional characteristics associated with time spent on tasks and the number of different tasks performed, and the association between time spent on tasks and job satisfaction. Results: FP (n=155) spent a mean of 143.6 min/day (95% CI 135.2 to 152.0) performing tasks such as prescription refills, teaching, meetings, management and communication with other professionals (33.4% of their workload). FP with larger patient lists spent less time on these tasks (p=0.002). Older FP (p=0.021) and those with larger lists (p=0.011) performed fewer tasks. The mean job satisfaction score was 3.5 (out of 5). No association was found between job satisfaction and time spent on tasks. Conclusions: FP spent one-third of their workday in coordinating care, teaching and managing. Time devoted to these tasks decreases with increasing list size and physician age.

  6. d

    Hospital Admitted Patient Care Activity

    • digital.nhs.uk
    Updated Sep 21, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2023). Hospital Admitted Patient Care Activity [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity
    Explore at:
    Dataset updated
    Sep 21, 2023
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Apr 1, 2022 - Mar 31, 2023
    Description

    This publication reports on Admitted Patient Care activity in England for the financial year 2022-23. This report includes but is not limited to analysis of hospital episodes by patient demographics, diagnoses, external causes/injuries, operations, bed days, admission method, time waited, specialty, provider level analysis and Adult Critical Care (ACC). It describes NHS Admitted Patient Care Activity, Adult Critical Care activity and performance in hospitals in England. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care and may also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. The data sources for this publication are Hospital Episode Statistics (HES). It contains final data and replaces the provisional data that are released each month. HES contains records of all admissions, appointments and attendances for patients at NHS hospitals in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore, this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Please send queries or feedback via email to enquiries@nhsdigital.nhs.uk. Author: Secondary Care Open Data and Publications, NHS England. Lead Analyst: Emily Michelmore

  7. Number of hours per week U.S. dermatologists spend seeing patients 2018

    • statista.com
    Updated Nov 30, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2023). Number of hours per week U.S. dermatologists spend seeing patients 2018 [Dataset]. https://www.statista.com/statistics/664139/dermatologist-patient-hours-spent-per-week-us/
    Explore at:
    Dataset updated
    Nov 30, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 21, 2017 - Feb 21, 2018
    Area covered
    United States
    Description

    This statistic shows the number of hours that dermatologists in the U.S. spend per week seeing patients as of 2018. It was found that 76 percent of dermatologists spend 30 to 45 hours per week seeing patients.

  8. Primary care physicians' weekly hours with patients, select countries 2019,...

    • statista.com
    Updated Nov 30, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2023). Primary care physicians' weekly hours with patients, select countries 2019, by gender [Dataset]. https://www.statista.com/statistics/1094987/primary-care-physician-weekly-patient-contact-hours-by-gender/
    Explore at:
    Dataset updated
    Nov 30, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 18, 2018 - Apr 10, 2019
    Area covered
    Worldwide
    Description

    According to a survey of practicing physicians in various countries, primary care physicians in France spent the most time seeing patients, as male physicians reported 45 hours per week and female physicians reported 43 hours per week. This statistic shows the number of hours per week primary care physicians spent seeing patients in select countries worldwide in 2019, by gender.

  9. IH157 - All persons aged 15 and over, time spent walking on a typical day

    • datasalsa.com
    csv, json-stat, px +1
    Updated Jul 9, 2021
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Central Statistics Office (2021). IH157 - All persons aged 15 and over, time spent walking on a typical day [Dataset]. https://datasalsa.com/dataset/?catalogue=data.gov.ie&name=ih157-all-persons-aged-15-and-over-time-spent-walking-on-a-typical-day
    Explore at:
    csv, px, json-stat, xlsxAvailable download formats
    Dataset updated
    Jul 9, 2021
    Dataset provided by
    Central Statistics Office Irelandhttps://www.cso.ie/en/
    Authors
    Central Statistics Office
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jul 9, 2021
    Description

    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...

  10. COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries...

    • healthdata.gov
    • datahub.hhs.gov
    • +2more
    Updated May 3, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Department of Health & Human Services (2024). COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries (RAW) [Dataset]. https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh
    Explore at:
    csv, application/rssxml, application/rdfxml, tsv, xml, kmz, application/geo+json, kmlAvailable download formats
    Dataset updated
    May 3, 2024
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Authors
    U.S. Department of Health & Human Services
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

    The following dataset provides state-aggregated data for hospital utilization in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities and (3) National Healthcare Safety Network (before July 15).

    The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

    No statistical analysis is applied to account for non-response and/or to account for missing data.

    The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

    On April 27, 2022 the following pediatric fields were added:

  11. all_pediatric_inpatient_bed_occupied
  12. all_pediatric_inpatient_bed_occupied_coverage
  13. all_pediatric_inpatient_beds
  14. all_pediatric_inpatient_beds_coverage
  15. previous_day_admission_pediatric_covid_confirmed_0_4
  16. previous_day_admission_pediatric_covid_confirmed_0_4_coverage
  17. previous_day_admission_pediatric_covid_confirmed_12_17
  18. previous_day_admission_pediatric_covid_confirmed_12_17_coverage
  19. previous_day_admission_pediatric_covid_confirmed_5_11
  20. previous_day_admission_pediatric_covid_confirmed_5_11_coverage
  21. previous_day_admission_pediatric_covid_confirmed_unknown
  22. previous_day_admission_pediatric_covid_confirmed_unknown_coverage
  23. staffed_icu_pediatric_patients_confirmed_covid
  24. staffed_icu_pediatric_patients_confirmed_covid_coverage
  25. staffed_pediatric_icu_bed_occupancy
  26. staffed_pediatric_icu_bed_occupancy_coverage
  27. total_staffed_pediatric_icu_beds
  28. total_staffed_pediatric_icu_beds_coverage

    On January 19, 2022, the following fields have been added to this dataset:
  29. inpatient_beds_used_covid
  30. inpatient_beds_used_covid_coverage

    On September 17, 2021, this data set has had the following fields added:
  31. icu_patients_confirmed_influenza,
  32. icu_patients_confirmed_influenza_coverage,
  33. previous_day_admission_influenza_confirmed,
  34. previous_day_admission_influenza_confirmed_coverage,
  35. previous_day_deaths_covid_and_influenza,
  36. previous_day_deaths_covid_and_influenza_coverage,
  37. previous_day_deaths_influenza,
  38. previous_day_deaths_influenza_coverage,
  39. total_patients_hospitalized_confirmed_influenza,
  40. total_patients_hospitalized_confirmed_influenza_and_covid,
  41. total_patients_hospitalized_confirmed_influenza_and_covid_coverage,
  42. total_patients_hospitalized_confirmed_influenza_coverage

    On September 13, 2021, this data set has had the following fields added:
  43. on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses,
  44. on_hand_supply_therapeutic_b_bamlanivimab_courses,
  45. on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses,
  46. previous_week_therapeutic_a_casirivimab_imdevimab_courses_used,
  47. previous_week_therapeutic_b_bamlanivimab_courses_used,
  48. previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used

    On June 30, 2021, this data set has had the following fields added:
  49. deaths_covid
  50. deaths_covid_coverage

    On April 30, 2021, this data set has had the following fields added:
  51. previous_day_admission_adult_covid_confirmed_18-19
  52. previous_day_admission_adult_covid_confirmed_18-19_coverage
  53. previous_day_admission_adult_covid_confirmed_20-29_coverage
  54. previous_day_admission_adult_covid_confirmed_30-39
  55. previous_day_admission_adult_covid_confirmed_30-39_coverage
  56. previous_day_admission_adult_covid_confirmed_40-49
  57. previous_day_admission_adult_covid_confirmed_40-49_coverage
  58. previous_day_admission_adult_covid_confirmed_40-49_coverage
  59. previous_day_admission_adult_covid_confirmed_50-59
  60. previous_day_admission_adult_covid_confirmed_50-59_coverage
  61. previous_day_admission_adult_covid_confirmed_60-69
  62. previous_day_admission_adult_covid_confirmed_60-69_coverage
  63. previous_day_admission_adult_covid_confirmed_70-79
  64. previous_day_admission_adult_covid_confirmed_70-79_coverage
  65. previous_day_admission_adult_covid_confirmed_80+
  66. previous_day_admission_adult_covid_confirmed_80+_coverage
  67. previous_day_admission_adult_covid_confirmed_unknown
  68. previous_day_admission_adult_covid_confirmed_unknown_coverage
  69. previous_day_admission_adult_covid_suspected_18-19
  70. previous_day_admission_adult_covid_suspected_18-19_coverage
  71. previous_day_admission_adult_covid_suspected_20-29
  72. previous_day_admission_adult_covid_suspected_20-29_coverage
  73. previous_day_admission_adult_covid_suspected_30-39
  74. previous_day_admission_adult_covid_suspected_30-39_coverage
  75. previous_day_admission_adult_covid_suspected_40-49
  76. previous_day_admission_adult_covid_suspected_40-49_coverage
  77. previous_day_admission_adult_covid_suspected_50-59
  78. previous_day_admission_adult_covid_suspected_50-59_coverage
  79. previous_day_admission_adult_covid_suspected_60-69
  80. previous_day_admission_adult_covid_suspected_60-69_coverage
  81. previous_day_admission_adult_covid_suspected_70-79
  82. previous_day_admission_adult_covid_suspected_70-79_coverage
  83. previous_day_admission_adult_covid_suspected_80+
  84. previous_day_admission_adult_covid_suspected_80+_coverage
  85. previous_day_admission_adult_covid_suspected_unknown
  86. previous_day_admission_adult_covid_suspected_unknown_coverage

  • Required travel time to patients for caregivers in the U.S. as of 2019

    • ai-chatbox.pro
    • statista.com
    Updated Dec 18, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista Research Department (2023). Required travel time to patients for caregivers in the U.S. as of 2019 [Dataset]. https://www.ai-chatbox.pro/?_=%2Ftopics%2F4113%2Fcaregivers-in-the-us%2F%23XgboD02vawLYpGJjSPEePEUG%2FVFd%2Bik%3D
    Explore at:
    Dataset updated
    Dec 18, 2023
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    United States
    Description

    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.

  • Cameroon CM: Proportion of Time Spent on Unpaid Domestic and Care Work:...

    • ceicdata.com
    Updated Jan 12, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2021). Cameroon CM: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day [Dataset]. https://www.ceicdata.com/en/cameroon/health-statistics/cm-proportion-of-time-spent-on-unpaid-domestic-and-care-work-female--of-24-hour-day
    Explore at:
    Dataset updated
    Jan 12, 2021
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2014
    Area covered
    Cameroon
    Description

    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; ;

  • r

    Data and code for: Better self-care through co-care? A latent profile...

    • researchdata.se
    • demo.researchdata.se
    Updated Aug 19, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Carolina Wannheden; Marta Roczniewska; Henna Hasson; Klas Karlgren; Ulrica von Thiele Schwarz (2024). Data and code for: Better self-care through co-care? A latent profile analysis of primary care patients’ experiences of e-health-supported chronic care management [Dataset]. http://doi.org/10.48723/kzja-5k21
    Explore at:
    (19302), (61550), (4737), (3482), (12552), (38887), (3082)Available download formats
    Dataset updated
    Aug 19, 2024
    Dataset provided by
    Karolinska Institutet
    Authors
    Carolina Wannheden; Marta Roczniewska; Henna Hasson; Klas Karlgren; Ulrica von Thiele Schwarz
    Time period covered
    Oct 2018 - Jun 2019
    Area covered
    Stockholm County
    Description

    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.

  • France FR: Proportion of Time Spent on Unpaid Domestic and Care Work: Male:...

    • ceicdata.com
    Updated Feb 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2025). France FR: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day [Dataset]. https://www.ceicdata.com/en/france/health-statistics/fr-proportion-of-time-spent-on-unpaid-domestic-and-care-work-male--of-24-hour-day
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2010
    Area covered
    France
    Description

    France FR: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data was reported at 9.650 % in 2010. France FR: Proportion of Time Spent on Unpaid Domestic and Care Work: Male: % of 24 Hour Day data is updated yearly, averaging 9.650 % from Dec 2010 (Median) to 2010, with 1 observations. France FR: 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 France – Table FR.World Bank: 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; ;

  • d

    SHMI site change during spell contextual indicator

    • digital.nhs.uk
    csv, pdf, xls, xlsx
    Updated Jul 13, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2023). SHMI site change during spell contextual indicator [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/shmi/2023-07
    Explore at:
    pdf(226.8 kB), csv(8.8 kB), xls(73.2 kB), xlsx(32.0 kB)Available download formats
    Dataset updated
    Jul 13, 2023
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Mar 1, 2022 - Feb 28, 2023
    Area covered
    England
    Description

    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) has not submitted data to the Secondary Uses Service (SUS) since June 2022 due to an issue with their patient records system. This is causing a large shortfall in records with data only submitted for 4 months out of the 12 months in the current time period. Values for this trust should be viewed in the context of this issue. 5. A number of trusts are currently engaging in a pilot to submit Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS), rather than the Admitted Patient Care (APC) dataset. As the SHMI is calculated using APC data, this does have the potential to impact on the SHMI value for these trusts. Trusts with SDEC activity removed from the APC data have generally seen an increase in the SHMI value. This is because the observed number of deaths remains approximately the same as the mortality rate for this cohort is very low; secondly, the expected number of deaths decreases because a large number of spells are removed, all of which would have had a small, non-zero risk of mortality contributing to the expected number of deaths. We are working to better understand the planned changes to the recording of SDEC activity and the potential impact on the SHMI. The trusts affected in this publication are: Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), 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), and University Hospitals of Derby and Burton NHS Foundation Trust (trust code RTG). 6. 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...

    • ceicdata.com
    Updated Jan 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2025). Mexico MX: Time Spent Dealing with the Requirements of Government Regulations: % of Senior Management Time [Dataset]. https://www.ceicdata.com/en/mexico/company-statistics/mx-time-spent-dealing-with-the-requirements-of-government-regulations--of-senior-management-time
    Explore at:
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2006 - Dec 1, 2010
    Area covered
    Mexico
    Variables measured
    Enterprises Statistics
    Description

    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;

  • Chile CL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female:...

    • ceicdata.com
    Updated Jan 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2025). Chile CL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day [Dataset]. https://www.ceicdata.com/en/chile/social-health-statistics/cl-proportion-of-time-spent-on-unpaid-domestic-and-care-work-female--of-24-hour-day
    Explore at:
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2008 - Dec 1, 2015
    Area covered
    Chile
    Description

    Chile CL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data was reported at 22.104 % in 2015. This records an increase from the previous number of 15.927 % for 2008. Chile CL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data is updated yearly, averaging 19.015 % from Dec 2008 (Median) to 2015, with 2 observations. The data reached an all-time high of 22.104 % in 2015 and a record low of 15.927 % in 2008. Chile CL: 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 Chile – Table CL.World Bank.WDI: Social: 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. 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/].

  • f

    Dataset of patient survey.

    • plos.figshare.com
    application/csv
    Updated Apr 18, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Caroline De Schacht; Gustavo Amorim; Lázaro Calvo; Efthymios Ntasis; Sara Van Rompaey; Julieta Matsimbe; Samuel Martinho; Erin Graves; Maria Fernanda Sardella Alvim; Ann Green; Hidayat Kassim; Inoque Carlos Carlos; C. William Wester; Carolyn M. Audet (2024). Dataset of patient survey. [Dataset]. http://doi.org/10.1371/journal.pone.0299282.s006
    Explore at:
    application/csvAvailable download formats
    Dataset updated
    Apr 18, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Caroline De Schacht; Gustavo Amorim; Lázaro Calvo; Efthymios Ntasis; Sara Van Rompaey; Julieta Matsimbe; Samuel Martinho; Erin Graves; Maria Fernanda Sardella Alvim; Ann Green; Hidayat Kassim; Inoque Carlos Carlos; C. William Wester; Carolyn M. Audet
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  • G

    Average time spent being physically active

    • open.canada.ca
    • www150.statcan.gc.ca
    • +2more
    csv, html, xml
    Updated Jan 17, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statistics Canada (2023). Average time spent being physically active [Dataset]. https://open.canada.ca/data/en/dataset/46ac5048-f79e-41db-9ca0-893a0603c692
    Explore at:
    csv, html, xmlAvailable download formats
    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

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

  • Daily average time spent at various locations, by population cohorts, 1992...

    • www150.statcan.gc.ca
    • datasets.ai
    • +3more
    Updated Dec 23, 2002
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Government of Canada, Statistics Canada (2002). Daily average time spent at various locations, by population cohorts, 1992 and 1998, inactive [Dataset]. http://doi.org/10.25318/4510000201-eng
    Explore at:
    Dataset updated
    Dec 23, 2002
    Dataset provided by
    Government of Canadahttp://www.gg.ca/
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

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

  • Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2023). Number of minutes U.S. dermatologists spend with each patient 2018 [Dataset]. https://www.statista.com/statistics/664165/dermatologist-minutes-with-patients-us-by-gender/
    Organization logo

    Number of minutes U.S. dermatologists spend with each patient 2018

    Explore at:
    Dataset updated
    Nov 30, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 21, 2017 - Feb 21, 2018
    Area covered
    United States
    Description

    This statistic shows the number of minutes that dermatologists in the U.S. spend with each patient as of 2018. It was found that 42 percent of dermatologists spend an average of between 9 to 12 minutes with each patient.

    Search
    Clear search
    Close search
    Google apps
    Main menu