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Select language Select language English Home Health Topics All topics » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Resources » Fact sheets Facts in pictures Multimedia Publications Questions & answers Tools and toolkits Popular » Coronavirus disease (COVID-19) Ebola virus disease Air pollution Hepatitis Top 10 causes of death World Health Assembly » Countries All countries » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Regions » Africa Americas South-East Asia Europe Eastern Mediterranean Western Pacific WHO in countries » Statistics Cooperation strategies Yemen crisis » Sadeq Al Wesabi Hasan An internally displaced family in a camp in Hudaydah © Credits   Newsroom All news » News releases Statements Campaigns Commentaries Events Feature stories Speeches Spotlights Newsletters Photo library Media distribution list Headlines » Timeline: WHO's COVID-19 response »   Emergencies Focus on » COVID-19 pandemic Ebola virus disease outbreak DRC 2021 Syria crisis Crisis in Northern Ethiopia Afghanistan Crisis Latest » Disease Outbreak News Travel advice Situation reports Weekly Epidemiological Record WHO in emergencies » Surveillance Research Funding Partners Operations Independent Oversight and Advisory Committee Health Emergency Dashboard » WHO © Credits Data Data at WHO » Global Health Estimates Health SDGs Mortality Triple billion targets Data collections Dashboards » COVID-19 Dashboard Triple Billion Dashboard Health Equity monitor Mortality Highlights » GHO SCORE Insights and visualizations Data collection tools Reports World Health Statistics 2021 » WHO © Credits About WHO About WHO » People Teams Structure Partnerships Collaborating Centres Networks, committees and advisory groups Transformation Our Work » General Programme of Work WHO Academy Activities Initiatives Funding » Assessed contributions Flexible funding WHO Foundation Accountability » Audit Budget Financial statements Programme Budget Portal Results Report Governance » World Health Assembly Executive Board Election of Director-General Governing Bodies website Home / Tools and toolkits / Child growth standards / Software Child growth standards This web site presents the WHO Child Growth Standards. (...) The first two modules concern the calculation of z-scores (or percentiles) for the assessment of individual child’s growth, and thus very pertinent for clinical application. (...) They can still be used for the calculation of z-scores and prevalence estimates (not confidence intervals).
Language:English
Score: 1084671.5 - https://www.who.int/toolkits/child-growth-standards/software
Data Source: un
However, in 1999 the ratio of total non-governmental credit to GDP in all transition economies remained below their estimated market-economy benchmarks for the provision of credit to the non-governmental sector. (...) This indicator provides a ranking of progress in liberalisation and institutional reform of the banking sector, on a scale of 1 to 4* (4.3). A score of 1 represents little change from a socialist banking system apart from the separation of the central bank and commercial banks, while a score of 2 means that a country has established internal currency convertibility and has liberalised significantly both interest rates and credit allocation. (...) A score of 1 represents little or no progress in hardening enterprise budget constraints and in requiring sound corporate governance practices, while a score of 2 means that there has been some progress in curbing directed credits and producer subsidies but with little enforcement of bankruptcy laws.
Language:English
Score: 1083184.2 - https://unece.org/fileadmin/DAM/ead/misc/ffd2001/fries.pdf
Data Source: un
Telemedicine increases access to specialist health care in rural areas. Credit: SIHI Philippines Building the evidence base on social innovation in health started as a global initiative and is now being implemented in TDR-supported hubs such as the University of the Philippines Manila. (...) Health workers report higher motivation and ability to deliver quality care to their communities through the digital connections. Score card for health : the Seal of Health Governance project in the Del Carmen local government unit keeps score on how cities and local areas implement health measures. The scores focus on a range of health markers from malnutrition rates, to incidence of infectious diseases, to access to sanitation, to health budget resources.
Language:English
Score: 1080079.1 - https://www.who.int/tdr/news/2...ocial-innovation-in-health/en/
Data Source: un
Step-wise approach for the selection of project-specific baselines A project developer seeking to claim credit for reducing emissions, needs to manage limited resources to select the most credible baseline from a potentially large pool of baseline options. (...) Relevant How project Desc of barrier Desc of barrier Desc of barrier Desc of barrier barrier I addresses it relevance/ relevance/ relevance/ relevance/ score score score score score Relevant How project Desc of barrier Desc of barrier Desc of barrier Desc of barrier barrier II addresses it relevance/ relevance/ relevance/ relevance/ score score score score score User Total score Sum score Sum score Sum score Sum score Sum scoreentry fields Selecting a baseline 17 ing a possible baseline option from further considera- tion, i.e. by documenting: ● Which mandatory requirements, i.e. related to local emissions, technology, performance standard, process, emissions, or land-use management that are customary within the local sector are not met by the possible baseline option; ● Which key resource is not locally available and can- not be made economically available through project design; or ● Which climatic, geographical or other circumstances exists that cannot be overcome through project design. (...) Rank plausible candidates and proposed project in order of increasing barrier relevance and assign increasing scores 5. Aggregate scores over all barriers for each candi- date and the project (only applies if more than one barrier is identified) 6.
Language:English
Score: 1074042.6 - https://www.unido.org/sites/de...tiple_project_categories_0.pdf
Data Source: un
Select language Select language English العربية 中文 Français Русский Español Português Home Health Topics All topics » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Resources » Fact sheets Facts in pictures Multimedia Publications Questions & answers Tools and toolkits Popular » Coronavirus disease (COVID-19) Ebola virus disease Air pollution Hepatitis Top 10 causes of death World Health Assembly » Countries All countries » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Regions » Africa Americas South-East Asia Europe Eastern Mediterranean Western Pacific WHO in countries » Statistics Cooperation strategies Yemen crisis » Sadeq Al Wesabi Hasan An internally displaced family in a camp in Hudaydah © Credits   Newsroom All news » News releases Statements Campaigns Commentaries Events Feature stories Speeches Spotlights Newsletters Photo library Media distribution list Headlines » Timeline: WHO's COVID-19 response »   Emergencies Focus on » COVID-19 pandemic Ebola virus disease outbreak DRC 2021 Syria crisis Crisis in Northern Ethiopia Afghanistan Crisis Latest » Disease Outbreak News Travel advice Situation reports Weekly Epidemiological Record WHO in emergencies » Surveillance Research Funding Partners Operations Independent Oversight and Advisory Committee Health Emergency Dashboard » WHO © Credits Data Data at WHO » Global Health Estimates Health SDGs Mortality Triple billion targets Data collections Dashboards » COVID-19 Dashboard Triple Billion Dashboard Health Equity monitor Mortality Highlights » GHO SCORE Insights and visualizations Data collection tools Reports World Health Statistics 2021 » WHO © Credits About WHO About WHO » People Teams Structure Partnerships Collaborating Centres Networks, committees and advisory groups Transformation Our Work » General Programme of Work WHO Academy Activities Initiatives Funding » Assessed contributions Flexible funding WHO Foundation Accountability » Audit Budget Financial statements Programme Budget Portal Results Report Governance » World Health Assembly Executive Board Election of Director-General Governing Bodies website Home / News / item / WHO encourages manufacturers to develop quality assured formulations of the game-changing drug rifapentine WHO encourages manufacturers to develop quality assured formulations of the game-changing drug rifapentine 15 July 2021 Departmental news Reading time: The Invitation to Manufacturers of Antituberculosis Medicines to submit an Expression of Interest for Product Evaluation to the WHO Prequalification Unit  has been updated to include key formulations of rifapentine for use in children and adults, following a request by the WHO Global Tuberculosis Programme.  (...) The WHO Global TB Programme has also submitted applications for the inclusion of the rifapentine 300 mg scored tablet and a fixed-dose combination (FDC) of rifapentine and isoniazid (300 mg/300 mg) in the  WHO Model List of Essential Medicines (EML) . (...) For adults, a  rifapentine 300 mg scored tablet   formulation : This stands out as the most versatile formulation for meeting dosing requirements of current regimens for TPT and the treatment of drug-susceptible TB with the rifapentine-containing 4-month regimen.
Language:English
Score: 1070930 - https://www.who.int/news/item/...game-changing-drug-rifapentine
Data Source: un
All items in the WHODAS 2.0 were tested against the Partial Credit Model for ordinality. The paired t -test was used for assessing the responsiveness of WHODAS 2.0 scores to clinical intervention. (...) As a result, two ways to compute the summary scores, namely simple and complex scoring, were found useful. (...) Simple scoring is as practical as hand scoring and may be preferable for busy clinical settings or interviews.
Language:English
Score: 1069705.7 - https://www.who.int/bulletin/volumes/88/11/09-067231/en/
Data Source: un
Before extending SCORE into more enterprises, Huzhou Department of Administration and Work Safety initiated the midterm workshop to reflect the progress of the SCORE Project in these 50 enterprises, in particular the achievements, challenges and opportunities in the course of SCORE Training at workplace level. (...) As everyone knows that SCORE is not the end, rather a means and a methodology. (...) Dai highly appraised the efforts done by the Department of Administration of Work Safety at different levels to incorporate SCORE methodology into work safety standardization program, work safety credit mechanism, and the explorative practices such as ‘Sihua Management’ in Nanxun, establishment of the risk inspection and management system in Deqing and informatization construction in Anji and so forth.
Language:English
Score: 1065027.3 - https://www.ilo.org/beijing/in...WCMS_596066/lang--en/index.htm
Data Source: un
Select language Select language English العربية 中文 Français Русский Español Português Home Health Topics All topics » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Resources » Fact sheets Facts in pictures Multimedia Publications Questions & answers Tools and toolkits Popular » Coronavirus disease (COVID-19) Ebola virus disease Air pollution Hepatitis Top 10 causes of death World Health Assembly » Countries All countries » A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Regions » Africa Americas South-East Asia Europe Eastern Mediterranean Western Pacific WHO in countries » Statistics Cooperation strategies Ukraine emergency Ukraine emergency » WHO / Blink Media - Brendan Hoffman © Credits   Newsroom All news » News releases Statements Campaigns Commentaries Events Feature stories Speeches Spotlights Newsletters Photo library Media distribution list Headlines » Timeline: WHO's COVID-19 response »   Emergencies Focus on » Afghanistan crisis COVID-19 pandemic Northern Ethiopia crisis Syria crisis Ukraine emergency Latest » Disease Outbreak News Travel advice Situation reports Weekly Epidemiological Record WHO in emergencies » Surveillance Research Funding Partners Operations Independent Oversight and Advisory Committee Health Emergency Dashboard » WHO © Credits Data Data at WHO » Global Health Estimates Health SDGs Mortality Database Data collections Dashboards » COVID-19 Dashboard Triple Billion Dashboard Health Equity Monitor Highlights » Global Health Observatory SCORE Insights and visualizations Data collection tools Reports » World Health Statistics 2022 COVID excess deaths DDI IN FOCUS: 2022 About WHO About WHO » People Teams Structure Partnerships Collaborating centres Networks, committees and advisory groups Transformation Our Work » General Programme of Work WHO Academy Activities Initiatives Funding » Investment case WHO Foundation Accountability » Audit Budget Financial statements Programme Budget Portal Results Report Governance » World Health Assembly Executive Board Election of Director-General Governing Bodies website Home / Publications / i / item / Integrated care for older people (‎ICOPE)‎ implementation framework: guidance for systems and services - Select language - العربية 中文 français русский español português Integrated care for older people (‎ICOPE)‎ implementation framework: guidance for systems and services 1 January 2019  |  Guidance (normative) Download (761.9 kB) Overview Alongside supporting community-level services, the Integrated Care for Older People (ICOPE) approach helps broader health and social care systems effectively respond to the diverse and complex needs of older people. (...) The ICOPE Implementation Framework provides a score card to help assess the overall capacity of health and social care services and systems to deliver integrated care in community settings and support the development of ICOPE implementation action plans. There are 19 actions needed to implement ICOPE on the services level (meso) and systems level (macro). The scoring process provides an evidence-based means of highlighting areas for improvement as well as establishing concrete measures of future improvements.
Language:English
Score: 1062287.6 - https://www.who.int/publications/i/item/9789241515993
Data Source: un
IMPROVING SME COMPETITIVENESS : ACCESS TO FINANCE AND E-FINANCE : NOTE / BY THE UNCTAD SECRETARIAT
Risk can be reduced by means of various risk management techniques such as credit scoring, external credit rating and risk self-assessment, all of which could be better applied through the use of cost-saving information technologies. (...) Modern Internet-based automated data mining technologies are making it possible to build up huge credit information databases and apply modern credit analysis and related credit appraisal, scoring and rating techniques. (...) TD/B/COM.3/43 Page 8 26. Developing online credit information and credit scoring and rating databases for SMEs from developing and transition economies should also become an important element of international capacity-building and technical assistance efforts.
Language:English
Score: 1061662.3 - daccess-ods.un.org/acce...t?open&DS=TD/B/COM.3/43&Lang=E
Data Source: ods
Impact evaluation design  4 year, longitudinal, experimental design – Randomization at cluster level (28 units) – Baseline (2007) and first follow up (2009 ) carried out by OPM – Second follow up (2011) by UNC and FAO  In 2011, added topics: – Sexual debut, partner characteristics, perceptions about peer behavior, marriage, pregnancy, mental health and risk – Economic activities  1811 households in second follow up (1783 used in our analysis) – 1280 treatment, 531 control – 18% attrition between R0 and R1; 5% between R1 and R2  Attrition was random Map of the evaluation sites Data limitations  Baseline and follow up data available on – Share of own production in total consumption – Ownership of livestock and agricultural implements (partial, and based on recall)  Only follow up data available on – Crop production, crop and livestock labor and input use, and credit use – Operation of non agricultural business – Participation and intensity of wage labor (overall, agricultural and non agricultural) and own farm labor  No data on – Time devoted to housework Agriculture is fundamental part of livelihoods of CT-OVC beneficiaries  Large majority are agricultural producers — Over 80% produce crops; over 75% have livestock  Most grow local maize and beans, using traditional technology and low levels of modern inputs  Most have low levels of assets – 2.6 acres of agricultural land, few small animals, basic agricultural tools and low levels of education  Only 16 percent used credit in 2011  1/4 of adults worked in wage labor, 1/2 in own agriculture, 1/3 in own business, 1/5 private transfers – Women more in agricultural wage labour – Almost all wage labour is casual  42% of children worked on family farm Framework for impact analysis  Difference-in-Difference (DD) estimator for those outcomes for which we have baseline data  Propensity score methods or Inverse probability weighting (IPW) for those outcomes for which we only have data in 2011 Difference-in-Difference (DD) estimator for those outcomes for which we have baseline data Which type of DD estimator to use? (...)  For individual level outcomes, estimate the p-score at the household or individual level? – Is it more appropriate, for individual labor supply, to estimate households p-score or individuals p-score (age, education, health, marital status, and prior work experience) 0 0.5 1 1.5 2 2.5 3 3.5 4 kd e n si ty p s 0 .2 .4 .6 .8 1 Estimated propensity score Treated Control 0 0.5 1 1.5 2 2.5 3 3.5 4 kd e n si ty p s 0 .2 .4 .6 .8 1 Estimated propensity score Treated Control Kernel density of the propensity score for the treated and control groups (i) Not weighted (ii) weighted Household level p-score Individual level p-score 0 1 2 3 0 .2 .4 .6 .8 1 prop score pdf of propensities for unweighted treatment obs pdf of propensities for unweighted control obs 0 1 2 3 4 0 .2 .4 .6 .8 1 prop score pdf of propensities for reweighted treatment obs pdf of propensities for reweighted control obs Individual labour supply results— IPW estimator Individual level weighting Model 1 Model 2 Model 3 Model 4 Treatment -0.026 -0.053* -0.138 -0.025 Treatment * distance to market 0.129** Treatment * age 0.005 Treatment * age squared -0.000 Treatment * chronic illness -0.004 District fixed effect YES YES YES YES Number of observations 3,643 3, 643 3, 643 3, 643 Adjusted R2 0.098 0.101 0. 098 0. 098 F-test of joint significance 2.17* 16.20*** 0.60 Impact on overall wage labor participation Note: *** p<0.01; ** p<0.05; * p<0.1. . – Overall, for all individuals, no impact on participation in wage labor – Large positive impact for those (particularly women) who live farther from markets Control variables: individual level (gender, age, education, health), hh level (gender, age, education, household size, dependency ratio, education), community indictors, district fixed effect Individual level results—IPW estimator Participation in wage and non wage labor Males: increasingly positive with age, both wage and own farm Females: More muted impact, reverses for ag wage labor Some of positive impact on own farm increasing with age may be due to chronic illness -20% -15% -10% -5% 0% 5% 10% 15% 20% 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 All wage labour OverallOverall Female Overall Female Male -20% -15% -10% -5% 0% 5% 10% 15% 20% 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 Agricultural wage labour Male OverallOverall FemaleFemaleFemale -20% -15% -10% -5% 0% 5% 10% 15% 20% 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 Non agricultural wage labour Male Overall Female -20% -15% -10% -5% 0% 5% 10% 15% 20% 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 Own farm labour Male Overall FemaleFemale Individual level results—IPW estimator Intensity of wage and non wage labor Overall: Negative impact on wage labor intensity—but negative impact is concentrated among chronically ill For both males and females, increasing intensity of own farm labor –substitution between wage and non wage labor? (...) – Mixed impact for hiring in labor  Non agricultural production – Positive impact on participation in nonfarm enterprise for female headed household, negative impact for male headed households  No impact on credit use Final remarks  Our first paper—a lot of growing pains  Despite data challenges, we find some interesting stories – Some impact on own farm production, but we can’t tell whether substitution in inputs is occurring – Impact on non farm business formation – Impact on labor supply decisions – Impact varies by gender  Choice of impact estimation methodology matters Thank You!
Language:English
Score: 1057850 - https://www.fao.org/fileadmin/...orkshops/Asfawkenyaresults.pdf
Data Source: un