Correlates of self-rated health among BIMA telemedicine customers in Ghana: A cross-sectional survey
Bibliographic record
Abstract
Background: Self-rated health (SRH) is a subjective predictor of morbidity and mortality. Nevertheless, little is known about its correlates among telemedicine users in low-resource settings, a population that may face unique health and access challenges. Objective: The study examined demographic and lifestyle factors associated with SRH among customers of BIMA’s telemedicine service in Ghana. Methods: We analysed cross-sectional secondary data from a telephone survey of BIMA customers (January 2022–June 2023). Variables included SRH, age, gender, medication use, tobacco use, physical activity (PA), and diet. A composite Healthy Life Score (HLS) combined PA and diet frequency. Multivariable logistic regression was used to examine the association between SRH and its correlates. Results: Females who formed majority of the 9,547 participants (61.4%) had a mean age of 35.1 (SD 11.6) years, Among them, higher HLS showed a dose–response association with good SRH (Average: aOR = 1.32, 95% CI: 1.06 -1.64; Good: aOR = 1.61, 95% CI: 1.29 –2.01; Excellent: aOR 2.17, 95% CI: 1.69 –2.78). Age had a small per-year effect (aOR = 0.99, 95% CI: 0.98 –0.99), which is meaningful cumulatively, resulting in approximately 10% lower odds over a decade. Men had higher odds than women (aOR = 1.14, 95% CI: 1.05 –1.24). Medication use (aOR = 0.58, 95% CI: 0.52 –0.65) and smoking (aOR = 0.70, 95% CI: 0.50 –0.98) were associated with lower odds of good SRH. Conclusion: Among BIMA telemedicine users in Ghana, SRH is closely linked to lifestyle and demographic factors. Integrating physical activity promotion, dietary counselling, and smoking cessation support into telemedicine consultations may enhance perceived health. However, findings should be interpreted with caution, given the reliance on self-reported data, non-validated HLS items, and the cross-sectional design of this study.
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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 | 0.001 |
| 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) | 0.002 | 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".