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Record W2769151876 · doi:10.1186/s12913-017-2745-7

Urban-rural difference in satisfaction with primary healthcare services in Ghana

2017· article· en· W2769151876 on OpenAlexaff
Sanni Yaya, Ghose Bishwajit, Michael Ekholuenetale, Vaibhav Shah, Bernard Kadio, Ogochukwu Udenigwe

Bibliographic record

VenueBMC Health Services Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
Fundersnot available
KeywordsHealth careHealth administrationMedicineNursing researchMilestoneDeveloping countryHealth informaticsHealth services researchRural areaPopulationSocioeconomic statusEnvironmental healthSocioeconomicsPublic healthNursingEconomic growthGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding regional variation in patient satisfaction about healthcare systems (PHCs) on the quality of services provided is instrumental to improving quality and developing a patient-centered healthcare system by making it more responsive especially to the cultural aspects of health demands of a population. Reaching to the innovative National Health Insurance Scheme (NHIS) in Ghana, surpassing several reforms in healthcare financing has been a milestone. However, the focus of NHIS is on the demand side of healthcare delivery. Studies focusing on the supply side of healthcare delivery, particularly the quality of service as perceived by the consumers are required. A growing number of studies have focused on regional differences of patient satisfaction in developed countries, however little research has been conducted concerning patient satisfaction in resource-poor settings like in Ghana. This study was therefore dedicated to examining the variation in satisfaction across rural and urban women in Ghana. METHODS: . Statistical significance was set at p < 0.05. RESULTS: The findings in this study revealed that about 57.1% were satisfied with primary health care services. The urban and rural areas reported 57.6 and 56.6% respectively which showed no statistically significant difference (z = 0.64; p = 0.523; 95%CI: -0.022, 0.043). Bivariate analysis showed that region, highest level of education, wealth index and type of facility were significantly associated with location of residence (urban-rural areas). After adjusting for confounding variables using logistic regression, geographical location became a key factor of satisfaction with primary healthcare services by location of residence. In urban areas, respondents from Greater Accra had 64% increase in the level of satisfaction when compared to those in Western region (OR = 1.64; 95CI: 1.09-2.47), Upper East had 75% increase in satisfaction compared to Western region (OR = 1.75; 95%CI: 1.08-2.84), Northern had an estimated 44% reduction in satisfaction when compared to Western region (OR = 0.56; 95%CI: 0.34-0.92). However, rural areas in Central, Volta, Eastern, Ashanti, Brong Aghafo, Northern and Upper West region had 51, 81, 69, 46, 62, 75 and 61% reduction respectively in the level of satisfaction when compared to Western region. CONCLUSIONS: Patient satisfaction is an important indicator of health outcomes. Quality of care and measuring level of patient satisfaction has been found to be the most useful tool to predict utilization and compliance. In fact, satisfied patients are more likely than unsatisfied ones to continue using health care services. Our results suggest that policymakers need to better understand the determinants of satisfaction with the health system and how different socio-demographic groups perceive satisfaction with healthcare services so as to address health inequalities between urban and rural areas within the same country.

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)0.002
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.144
GPT teacher head0.497
Teacher spread0.353 · 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

Citations88
Published2017
Admission routes1
Has abstractyes

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