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Record W4289133713 · doi:10.1080/27707571.2022.2103932

Factors associated with health-seeking behaviour among informal sector workers in the Kumasi metropolis of Ghana

2022· article· en· W4289133713 on OpenAlexaff
Dina Adei, Anthony Acquah Mensah, Williams Agyemang‐Duah, Lewis Aboagye-Gyasi

Bibliographic record

VenueCogent Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsQueen's University
Fundersnot available
KeywordsInformal sectorBenin cityEnvironmental healthHealth facilityHealth sectorSimple random sampleDeveloping countryHealth careBusinessSocioeconomicsMedicineHealth servicesEconomic growthPopulationSociologyFamily medicineEconomics

Abstract

fetched live from OpenAlex

Even though studies have established that informal sector workers are prone to occupational-related diseases, not much is known about their health-seeking behaviour. This study aims to examine drivers of health-seeking behaviour among informal sector workers in the Kumasi metropolis of Ghana. A cross-sectional survey was conducted. Simple random sampling technique was used to select 350 informal sector workers. Questionnaires were used to collect the data. The study revealed that 33.5% of the participants practiced good health-seeking behaviour when they developed occupational-related diseases in 2016. The study further revealed that informal sector workers with five or more dependents (AOR: 8.482; CI: 1.265–56.872; p = 0.028) and those who spent more than an hour at a health facility (AOR: 24.040; CI: 8.508–67.927; p < 0.0001) were more probable to exhibit good health-seeking behaviour. Informal sector workers without active National Health Insurance Scheme [NHIS] (AOR: 0.149; CI: 0.052–0.430; p < 0.0001) and employees were less likely to adopt good health-seeking behaviour. Given the limited formal healthcare system and resources available, socio-demographic factors should be taken into consideration when formulating policies to encourage informal sector workers to adopt good health-seeking behaviour.

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)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.248
GPT teacher head0.428
Teacher spread0.179 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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