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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
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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
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Integrated care for older people (ICOPE) implementation framework: guidance for systems and services
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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