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Record W2189361569

Health Hazards Associated with Spray Painting among Workers in Small Scale Auto Garages in Embakasi Division, Nairobi, Kenya

2011· dissertation· en· W2189361569 on OpenAlexfundno aff
Agnes K. Mwatu

Bibliographic record

VenueKenyatta University Institutional Repository (Kenyatta University) · 2011
Typedissertation
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
FundersAgency for Toxic Substances and Disease RegistryNational Institute for Occupational Safety and HealthCanadian Lung AssociationU.S. Department of Health and Human Services
KeywordsPaintingScale (ratio)PopulationMedicineEngineeringGeographyEnvironmental healthArtVisual artsCartography
DOInot available

Abstract

fetched live from OpenAlex

Most hazardous health effects of activities in small scale industries may not be apparent immediately, however they emerge much later in the life of the exposed individuals. One such small scale industrial activity, is spray painting in informal auto garages, popularly known in Kenya as “Jua Kali garages”. Although various disease symptoms may be associated with spray painting, respiratory and skin diseases are the major ones. The objective of this study therefore, was to establish occupational health hazards associated with spray painting in small scale auto garages in three selected locations of Embakasi division. To carry out the study, which took three months (June–August 2010), pre-tested questionnaires and checklists were administered to spray painters in the selected auto garages. Key informant individuals (KII) were interviewed to get details of disease symptoms and other issues to support the information captured by the questionnaires and checklists. A sample population of two hundred and seven spray painters was selected from small scale auto garages in the study area, their age ranged between 17-62 years, with 34% of the population being below 25 years. Half (51%) of the spray painters had been in this occupation for between 1-5 years. 65.3% of them had attained primary education, while the rest (34.7%) had secondary level of education. It was observed that, the main activities in the study garages were scraping off the old paints and spray painting. The two activities posed an exposure due to dust from old paints and over spray paint mists within the breathing zone of unprotected spray painters, and therefore data on asthmatic and bronchitis symptoms, and eye problems was collected, edited, coded and analyzed by using statistical package for social sciences (SPSS). Chi-square test of significance was used to measure association between the disease symptoms and exposure time, application methods, and amounts and types of paints. The analyzed data was presented using percentages, frequency tables and bar charts. Painters’ health seeking behaviours and presence of the disease symptoms associated with this occupation were also studied. Application methods had a significant relationship between asthmatic symptoms, (χ² = 18.72338; df = 2; p = 0.00009), but non between bronchitis symptoms (χ² = 0.055885; df = 2; p = 0.97246). Exposure time had no significant relationship between all disease symptoms in the study (asthmatic symptoms; χ² = 3.75855; df = 3; p = 0.28871, bronchitis; χ² = 6.4773; df = 3; p = 0.09056 and eye problems; χ ² = 2.33641; df = 3; p = 0.50558). Types and amounts of paint also had no significant relationship between all diseases symptoms. According to the study, this was due to onset of the disease symptoms within a short duration of exposure. 85.7% and 67.3% of all the spray painters had bronchitis and asthmatic symptoms respectively, while 49.3% had eye problems. This indicated a high prevalence of disease symptoms associated with spray painting among the spray painters in the study area, who also had poor health seeking behaviours. Health hazard awareness creation among all stakeholders was recommended to ensure health and safety of workers and further research in the field, especially effectiveness of interventions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.172
Teacher spread0.166 · 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 teacher head, not a consensus.

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

Citations2
Published2011
Admission routes1
Has abstractyes

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