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Record W6966384014 · doi:10.48336/111n-0230

Non-communicable diseases in Ghana: risk factors, prevalence and social support systems

2022· article· en· W6966384014 on OpenAlexaff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSocial supportSocioeconomic statusLogistic regressionNonprobability samplingThematic analysisSocial determinants of healthQualitative propertyQualitative researchExplanatory modelMultilevel model

Abstract

fetched live from OpenAlex

The aim of this thesis is to examine the effects of individual and neighborhood socioeconomic status (SES) on hypertension, establish an association between non-communicable diseases (NCDs) and disability and examine the role of social support systems in management of NCDs in Ghana. Specific focus is given to the burden of hypertension, diabetes, and stroke. The study is motivated by the scarcity of research on NCD-related risks among women in Ghana. Similarly, there is paucity of academic literature on relationships between NCDs, disability, and social support systems available to people living with these chronic conditions. In examining these issues, a mixed methods sequential explanatory research design is adopted using the World Health Organization’s Commission on Social Determinants of Health (CSDH) as a conceptual framework. Specifically, data from the Women’s Health Study of Accra (WHSA-I) and the Ghana Global Ageing on Adult Health Survey (SAGE) are analyzed using multilevel logistic regression and ordinary least squares (OLS) regression respectively. In addition, qualitative interviews were undertaken in two teaching hospitals in Ghana with 33 patients living with NCDs, who were recruited for the study using purposive sampling technique. Results from the quantitative analyses reveal that wealthy women are more likely to be hypertensive compared to poorer women. However, the effects of neighborhood SES/wealth was attenuated after adjusting for individual-level SES/wealth. In addition, respondents with higher education reported higher levels of disability compared to those with no education, while stroke emerge as the major contributor to disability among Ghanaians. Thematic analysis of the interview data further indicate that the nuclear family is the main source of social support for the self-management of NCDs. The results suggests that efforts aimed at the prevention, control and management of hypertension should focus on changing individual behavioral lifestyles. Health promotion programs should also focus individual factors (engaging in physical activity and adoption of healthy diet with an emphasis on fruit and vegetable consumption etc) in the prevention, control and management of hypertension in Ghana. This research also offers innovative contributions to the extant literature by confirming that the International Classification of Functioning, Disability and Health (ICF) can be effectively used to classify individuals with disabilities living in Ghana not only based on their medical conditions, but also by their functioning level. This research further demonstrates that the ICF framework can be used within the healthcare setting to promote inclusiveness within the broader community. Finally, the study findings demonstrates that social support can be used as a strategy in the promotion of the physical and mental well-being of these NCD patients.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.277
Teacher spread0.239 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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