Communication barriers to formal healthcare utilisation and associated factors among poor older people in Ghana
Why this work is in the frame
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Bibliographic record
Abstract
Background Successful interactions between healthcare users and healthcare providers are facilitated by effective communication, which is one of the functions of quality healthcare delivery. Whereas a lack of financial resources impedes healthcare utilisation, a lack of meaningful communication is also likely to create a barrier between healthcare providers and users.Method and materials In this study, we use bivariate and multivariate statistical analyses to model the likelihood of communication barriers to formal healthcare utilisation using socio-economic and demographic data collected from poor older people under the Livelihood Empowerment Against Poverty (LEAP) Programme in the Atwima Nwabiagya District of Ghana.Results The study finds that participants aged 85 years or above are significantly more likely to encounter communication barriers to formal healthcare utilisation (AOR: 1.575, C.I: 0.927–4.452). The results show that non-Akan participants are significantly more likely to encounter communication barriers to formal healthcare utilisation (AOR: 1.206, C.I: 0.507–2.869). Furthermore, we find that participants with high school education are significantly less likely to encounter communication barriers to formal healthcare utilisation (AOR: 0.189, C.I: 0.051–0.700).Conclusions Based on the findings we conclude that the provision of location-specific language access services would improve communication and reduce healthcare disparities in minority ethnic groups who are coexisting with a majority ethnic group. Thus, the findings strongly suggest the need for policy makers to recruit language translators in healthcare systems to partly eliminate communication barriers to healthcare utilisation. From a broader perspective, the study offers valuable knowledge for health policy design and amendment aimed at lessening communication barriers to formal healthcare utilisation.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it