Since 2000 a number of community-driven sanitation approaches have emerged that counter a historical trend to subsidise the provision of latrines to the poor. This study reports on a set of findings and conclusions concerning the effectiveness and sustainability of two such approaches operating in Zimbabwe, the community health club (CHC) approach and community-led total sanitation (CLTS). Surveys, interviews and focus groups were conducted in a total of ten project communities from three districts. Results show that, despite little resistance to the idea, a household's ability to own a latrine depends heavily on its ability to afford one. Affordability is also key in moving up the 'sanitation ladder', which is necessary if behaviour change is to be sustained in the long term. Whilst both approaches effectively encouraged measures that combat open defecation, only health clubs witnessed a significant increase in the adoption of hand washing.However, CLTS proved more effective in promoting latrine construction, suggesting that the emphasis the CHCs place on hygiene practices such as hand washing needs to be coupled with an even stronger focus on the issue of sanitation brought by CLTS.
Effective menstrual management is essential for the mental and physical well being of women. However, many women in low income countries lack access to the materials and facilities required. They are thus restricted in their activities whilst menstruating thus compromising their education, income, and domestic responsibilities. This study describes the menstrual management challenges faced by women in an emergency situation in Uganda. Fifty interviews and focus group discussions were conducted with women from villages, IDP camps, and schools so that the menstrual management of the host population could be compared with the IDPs. This study showed that in IDP camps there was a significant lack of materials including soap, underpants and absorbing cloth, and facilities like latrines and bathing shelters. As a consequence women in IDP camps suffer with poor health and diminished dignity. There is also a lack of education about menstruation and reproductive health and practices are strongly influenced by cultural taboos.
Target 7C of the Millennium Development Goals is to "halve, by 2015, the proportion of people without sustainable access to safe drinking water and basic sanitation". However, the corresponding indicator measures the "proportion of population using an improved drinking water source". This raises the question of whether "safe" and "improved" can be used interchangeably.
Since publication of the 3rd Edition of the World Health Organisation (WHO) Drinking Water Quality guidelines, global adoption of water safety plans (WSPs) has been gathering momentum.Most guidance lists managerial commitment and 'buy-in' as critical to the success of WSP implementation; yet the detail on how to generate it is lacking. This commentary discusses aspects of managerial commitment to WSPs. We argue that the public health motivator should be clearer and a paramount objective and not lost among other, albeit legitimate, drivers such as political or regulatory pressures and financial efficiency.
Reaction
classification has often been considered an important
task for many different applications, and has traditionally been accomplished
using hand-coded rule-based approaches. However, the availability
of large collections of reactions enables data-driven approaches to
be developed. We present the development and validation of a 336-class
machine learning-based classification model integrated within a Conformal
Prediction (CP) framework to associate reaction class predictions
with confidence estimations. We also propose a data-driven approach
for “dynamic” reaction fingerprinting to maximize the
effectiveness of reaction encoding, as well as developing a novel
reaction classification system that organizes labels into four hierarchical
levels (SHREC: Sheffield Hierarchical REaction Classification). We
show that the performance of the CP augmented model can be improved
by defining confidence thresholds to detect predictions that are less
likely to be false. For example, the external validation of the model
reports 95% of predictions as correct by filtering out less than 15%
of the uncertain classifications. The application of the model is
demonstrated by classifying two reaction data sets: one extracted
from an industrial ELN and the other from the medicinal chemistry
literature. We show how confidence estimations and class compositions
across different levels of information can be used to gain immediate
insights on the nature of reaction collections and hidden relationships
between reaction classes.
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