Challenges Confronting Rural Dwellers in Accessing Health Information in Ghana: Shai Osudoku District in Perspective
Bibliographic record
Abstract
The focus of the study was to investigate health information seeking behavior as well as the barriers to health information seeking among rural dwellers in Ghana using Shai Osudoku District as a case study. The convenient and purposive sampling technique was used to sample 210 community members within Shai Osudoku District. The Statistical Package for Social Sciences (SPSS) version 21.0 was employed to process the quantitative data. The data was processed into statistical tables and charts for interpretation and discussion. The outcome of the study revealed that the most common sources of health information seeking among rural community members in the district of investigation are posters, health care providers and families/friends, with radio being the most used platform. It was also revealed that those respondents with higher level of education are more likely to use the Internet and television in accessing health information (p = 0.001 and 0.000 respectively). Similarly, respondents with primary education or informal education were more likely to contact family members for health information (p = 0.001) The outcome of the study also shows that many rural communities in Ghana, particularly rural dwellers of Shai Osudoku District, face numerous challenges in accessing health information. Notable among them are language barrier, location of the villages and inaccessibility to emerging technologies such as mobile phones and television sets. We conclude that, policies for improving health information access and reducing barriers to health information seeking in rural communities should be designed and implemented by Ghana health service. Also, education on how to access health-related information with easily accessible sources either free or at low-priced could be a way to help people in rural settings in Ghana with limited health information.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".