Access to Primary Health Care Services for Persons with Physical Disabilities in Rural Ghana
Bibliographic record
Abstract
Background: A growing body of evidence has shown that persons with physical disabilities experience substantial barriers in accessing primary health care (PHC) services in rural areas. Negative attitudes from health care providers and inaccessible health care facilities and equipment are common experiences that negatively affect access to quality health care for persons with physical disabilities. However, there is limited research that explores this issue in rural Ghana. Objective: I carried out this dissertation in three chapters to address this research gap. Chapter 3 synthesized published literature to understand the factors affecting access to PHC for persons with disabilities (PWDs) in rural areas globally. Chapter 4 explored the experiences of persons with physical disabilities in accessing PHC services in rural Ghana. Chapter 5 highlighted the perspectives of health care providers in delivering PHC services to persons with physical disabilities in rural Ghana. Method: I employed framework synthesis and used framework analyses to analyze existing literature (chapter 3). I also used a qualitative descriptive design to conduct semi-structured interviews with 18 persons with physical disabilities (chapter 4) and 15 health care providers (chapter 5). I used thematic analysis to guide the analysis of the interviews. Results: In chapter 3, I found that PWDs were unable to access PHC due to obstacles including the interplay of four major factors; availability, acceptability, geography and affordability. Chapter 4 revealed that participants shared their experiences under two overarching themes: limited facilities and lack of providers. Chapter 5 uncovered three major themes: challenges in providing health care; strategies in navigating the challenges; and positive experiences in providing health care. Implications: The information provided in this dissertation is potentially important to policy makers and PHC providers as it presents evidence on the barriers and facilitators to PHC access in rural settings. In particular, understanding this topic from multiple sources (existing literature, PWDs and providers) will be critical for policy design and client-centered service delivery in rural Ghana and potentially other low- and middle-income countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".