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81 results for “Poverty”

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ClinicalTrials.gov32/100

ASHA Bangladesh--An Integrated Intervention to Address Poverty and Depression

ClinicalTrials.gov study NCT06295250. IPD Sharing: YES. Countries: 1. Publications: 131.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Child of Urban Poverty Iron Project (CUPIP) - A Pilot Study

ClinicalTrials.gov study NCT03819530. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Targeting Child Mental Health and Household Poverty

ClinicalTrials.gov study NCT05667675. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Patient Navigators for Children's Community Mental Health Services in High Poverty Urban Communities

ClinicalTrials.gov study NCT05294042. IPD Sharing: Not stated. Countries: 1. Publications: 6.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad32/100

Data from: The relationship between poverty and healthcare seeking among patients hospitalized with acute febrile illnesses in Chittagong, Bangladesh

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publicMay 2017View details →
dryad32/100

Data of subjective poverty, public services supply, and livelihood capital

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publicJul 2022View details →
dryad32/100

Supplemental Materials: Duration of poverty and subsequent cognitive function and decline among older adults in China, 2005-2018

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publicJun 2021View details →
zenodo28/100

The Effect of Perception of Poverty on Quality of Life in Old Women and the Mediation of Depression Dataset

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opencc-by-4.0Nov 2023View details →
zenodo28/100

THE POPULATION WISHES FOR POVERTY REDUCTION AND EMPLOYMENT

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opencc-by-4.0Dec 2023View details →
zenodo28/100

SPECIAL DIRECTIONS OF POVERTY REDUCTION IN THE REGIONS

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opencc-by-4.0Mar 2024View details →
zenodo28/100

SPECIAL DIRECTIONS OF POVERTY REDUCTION IN THE REGIONS

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opencc-by-4.0Mar 2024View details →
zenodo28/100

Globalization Poverty Inequality in Thailand

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opencc-by-4.0Mar 2024View details →
dryad28/100

The paradox of food production, consumption, poverty and malnutrition in Tanzania

<p><span><b>Background</b>: There have been claims amongst nutrition stakeholders in Tanzania that the food basket regions are leading in stunting prevalence. However, we could not find evidence that combines food production and stunting levels to substantiate this claim. Therefore, this study aims to compare data of stunting, food production and consumption at administrative regions in Tanzania Mainland.</span></p> <p><span><b>Methods</b>: The study used an ecological study design to show the relationship between stunting, poverty, and food production and consumption across administrative regions in Tanzania. The study used data from three national wide surveys: 2017/2018 Household Budget Survey (HBS), Tanzania National Nutrition Survey (TNNS) 2018 and Agriculture Statistics for Food Security report 2018/2019.</span></p> <p><span><b>Results</b>: The study showed that there is a positive relationship between the prevalence of stunting and food production (r=0.43, p=0.03) while there is a negative relationship between stunting and the level of both the average monthly household consumption expenditure (r = -0.48, p = 0.01) and average monthly household food consumption expenditure (r = -0.509, p = 0.01). It was further found that some regions which have higher levels of stunting such as Njombe have the lowest level of basic need poverty.</span></p> <p><span><b>Conclusion</b>: The study found a positive relationship between food production and the prevalence of stunting using data across regions in Mainland Tanzania. This is an indication that regional food security may not entail nutrition security hence a call for more advocacy on nutrition-sensitive agriculture.</span></p>

opencc-zeroDec 2021View details →
zenodo28/100

FDI and Poverty Reduction in Latin America

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opencc-by-4.0May 2024View details →
zenodo28/100

Figure 3 in The First Cases of Evolving Glyphosate Resistance in UK Poverty Brome (Bromus sterilis) Populations

Figure 3. Calculated glyphosate ED50 values for 11 field-collected B. sterilis populations from the United Kingdom. Error bars are standard error of ED50 parameter estimates.

opencc-by-4.0Oct 2018View details →
dryad28/100

Data from: Mental illness, poverty and stigma in India: a case control study

Objective: To assess the effect of experienced stigma on depth of multidimensional poverty of persons with severe mental illness (PSMI) in Delhi, India, controlling for gender, age and caste. Design: Matching case (hospital)–control (population) study. Setting: University Hospital (cases) and National Capital Region (controls), India. Participants: A case–control study was conducted from November 2011 to June 2012. 647 cases diagnosed with schizophrenia or affective disorders were recruited and 647 individuals of same age, sex and location of residence were matched as controls at a ratio of 1:2:1. Individuals who refused consent or provided incomplete interview were excluded. Main outcome measures: Higher risk of poverty due to stigma among PSMI. Results: 38.5% of PSMI compared with 22.2% of controls were found poor on six dimensions or more. The difference in multidimensional poverty index was 69% between groups with employment and income of the main contributors. Multidimensional poverty was strongly associated with stigma (OR 2.60, 95% CI 1.27 to 5.31), scheduled castes/scheduled tribes/other backward castes (2.39, 1.39 to 4.08), mental illness (2.07, 1.25 to 3.41) and female gender (1.87, 1.36 to 2.58). A significant interaction between stigma, mental illness and gender or caste indicates female PSMI or PSMI from 'lower castes' were more likely to be poor due to stigma than male controls (p&lt;0.001) or controls from other castes (p&lt;0.001). Conclusions: Public stigma and multidimensional poverty linked to SMI are pervasive and intertwined. In particular for low caste and women, it is a strong predictor of poverty. Exclusion from employment linked to negative attitudes and lack of income are the highest contributors to multidimensional poverty, increasing the burden for the family. Mental health professionals need to be aware of and address these issues.

opencc-zeroDec 2014View details →
zenodo28/100

Poverty by State

<p>Poverty rate by state 2021</p>

opencc-by-4.0Jan 2023View details →
ClinicalTrials.gov28/100

A Feasibility Study to Improve Colorectal Cancer Screening Among Racially Diverse Zip Codes in a Persistent Poverty County Using Navigation and Machine Learning Predictive Algorithms

ClinicalTrials.gov study NCT05383976. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov28/100

Parent-mediated Social-communication Therapy for Young Children Living in Poverty in Brazil

ClinicalTrials.gov study NCT05011240. IPD Sharing: NO. Countries: 0. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov28/100

Offering Free Fruits and Vegetables to Men at Risk of Poverty

ClinicalTrials.gov study NCT02787564. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

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International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

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Last verified 2026-04-29Open record

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Last verified 2026-04-29Open record