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Record W4405332751 · doi:10.3390/su162410873

The Impact of Poor Waste Management on Public Health Initiatives in Shanty Towns in Tanzania

2024· article· en· W4405332751 on OpenAlexaff
Felician Andrew Kitole, Temitope O. Ojo, Chijioke Emenike, Nolwazi Z. Khumalo, Khalid M. Elhindi, Hazem S. Kassem

Bibliographic record

VenueSustainability · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsDalhousie University
FundersKing Saud University
KeywordsBusinessSanitationPublic healthEnvironmental planningTanzaniaEnvironmental healthWaste disposalDeveloping countryEconomic growthMedicineGeographyWaste managementEngineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

Poor waste management in shanty towns across developing countries has significantly impacted public health, contributing to widespread outbreaks of diseases such as cholera, malaria, and typhoid due to unsanitary living conditions and contaminated environments. Limited efforts by residents and governments to implement effective waste disposal practices exacerbate these health risks, perpetuating a cycle of poor sanitation, increased disease transmission, and environmental degradation. This study investigates the impact of poor waste management on public health in informal settlements and explores strategies to mitigate these risks through improved practices and collaborative efforts. The study employed a cross-sectional research design and collected data using semi-structured questionnaires to collect data from 217 households in Tandale, Manzese, and Tandika in the Dar es Salaam region. The findings confirm that inadequate waste collection services, lack of proper disposal sites, high costs of waste management, and poor public awareness are key contributors to the accumulation of waste and the prevalence of diseases. Hypothesis testing further reveals that inadequate waste collection services significantly impact public health challenges, while public health initiatives on waste management significantly improve health outcomes and reduce disease prevalence. The study’s recommendations include increasing the frequency of waste collection, fostering community-led waste management initiatives, enhancing public education on the health risks of poor waste disposal, and providing subsidized resources such as waste bins and bags. Additionally, strengthened collaboration between local governments, NGOs, and community members is essential for mobilizing resources and implementing sustainable waste management practices. These measures are vital to reducing public health risks and creating healthier living conditions in underserved communities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.323
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2024
Admission routes1
Has abstractyes

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