Factors associated with barriers to patient-physician communication in specialist outpatient healthcare in Ghana
Bibliographic record
Abstract
Background Patients continue to encounter barriers to communication in specialist outpatient healthcare. However, little is known about the factors associated with patient-physician communication barriers in specialist outpatient healthcare in sub-Saharan Africa. The purpose of this study was to examine the factors that predict communication barriers among patients who use specialist outpatient healthcare in Ghana. Methods Using a stratified random sampling technique, the study was designed as a cross-sectional survey involving 269 patients at the Komfo Anokye Teaching Hospital in Kumasi. Chi-square, Fisher exact test, and binary logistic regression were used to analyse the data. Results Approximately 12% of the participants experienced communication barriers in their utilisation of specialist outpatient healthcare. The results revealed that unmarried (AOR: 3.244; C1 1.228–8.567, p = 0.018), non-Christians (AOR: 3.934; CI 1.342–11.532, p = 0.013), those with perceived poor health status (AOR: 3.680; CI 1.590–8.517, p = 0.002) and patients who spent over 180 min at the specialist outpatient department (AOR: 3.855; CI 1.659–8.962, p = 0.002) were significantly more likely to experience barriers to patient-physician communication. Conclusions These findings suggest the contribution of socio-demographic differences in barriers to patient-physician communication in specialist outpatient healthcare in Ghana. The study provides further evidence for barriers to effective communication between patients and subspecialty physicians in Ghana.
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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 | 0.008 |
| 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 | 0.000 |
| 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".