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Record W2774652505 · doi:10.1186/s12913-017-2644-y

Analysis of the impact of healthcare support initiatives for physically disabled people on their access to care in the city of Saint-Louis, Senegal

2017· article· en· W2774652505 on OpenAlexfundno aff
Diarra Bousso Senghor, Oumar Diop, Issa Sombié

Bibliographic record

VenueBMC Health Services Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
FundersAfrican Population and Health Research CenterInternational Development Research CentreDepartment of Social Services, Australian GovernmentJohn D. and Catherine T. MacArthur Foundation
KeywordsHealth careMedicineHealth informaticsHealth administrationPublic healthSocial WelfareNursingNursing researchPopulationEnvironmental healthGerontologyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: People with disabilities represent approximately 6% of the Senegalese population. They face significant barriers to accessing health care. Although several initiatives have been implemented to improve access to health care for this vulnerable population, few studies have examined the effects of these initiatives. We conducted a mixed methods study in three neighborhoods in Saint-Louis City (Senegal) to assess the impact of health systems and social assistance programs aimed at improving access to health care for people with disabilities. METHODS: Data were collected from 105 people living with disabilities aged 1-49 years (or their caregivers). Interviews were also conducted with key stakeholders in the health and welfare sectors. Global Positioning System (GPS) coordinates of all the health and social services within the city were obtained. We also conducted observations in the main regional hospital, the district health center and three level-one health facilities to assess physical accessibility as well as interactions between patients living with disabilities and health and social workers. Descriptive and multivariate analyses were performed using Sphinx software. Spatial data were used to make cartographic representations of the proximity to basic social services using Arc GIS software. RESULTS: Seventy-nine percent of survey respondents reported difficulty obtaining treatment. Key barriers to care included the high cost of care, as well as ill-treatment by health workers. Limited human resources and low levels of financial support, combined with logistical challenges were reported to hamper the success of social welfare initiatives that aim to facilitate access to health care for people with disabilities. CONCLUSION: Our results suggest that initiatives to increase access to health care among people with disability in Saint-Louis have had limited impact. Study findings underscore the importance of strengthening social assistance schemes within the health system and the need for social workers and health workers to collaborate to improve access to health care for people with disabilities.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.277
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.137
GPT teacher head0.546
Teacher spread0.409 · 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.

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

Citations12
Published2017
Admission routes1
Has abstractyes

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