Socio-economic determinants influencing adherence to secondary prophylaxis in patients with rheumatic heart disease: a systematic review
Bibliographic record
Abstract
Introduction: Rheumatic heart disease (RHD) poses a substantial global health challenge, especially impacting resource-limited nations, with over 40.5 million cases reported in 2019. The crucial role of Benzathine penicillin G in both primary and secondary prevention, particularly the latter, emphasizes its significance. Method: Following PRISMA guidelines, our systematic review explored Medline, Scopus, Google Scholar, and Embase databases from 1990 to 2022. Registered with PROSPERO ), the review utilized quality appraisal tools, including the PRISMA checklist, Cochrane bias tool and Newcastle-Ottawa scale. The objective was to identify and stratify the impact of socio-economic factors on adherence to secondary prophylaxis in RHD. Results and discussion: The impact of education on adherence has been found to be significant. Socially disadvantaged environments significantly influenced adherence, shaped by education, socio-economic status, and geographical location and access to healthcare. Surprisingly, lower education levels were associated with better adherence in certain cases. Factors contributing to decreased adherence included forgetfulness, injection-related fears, and healthcare provider-related issues. Conversely, higher adherence correlated with younger age, latent disease onset, increased healthcare resources, and easy access. Conclusion: Patient education and awareness were crucial for improving adherence. Structured frameworks, community initiatives, and outreach healthcare programs were identified as essential in overcoming barriers to secondary prophylaxis. Taking active steps to address obstacles like long-distance commute, waiting time, injection fears, and financial issues has the potential to greatly improve adherence. This, in turn, can lead to a more effective prevention of complications associated with RHD.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".