Communication barriers to formal healthcare utilisation and associated factors among poor older people in Ghana
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".