Systematic Literature Review on strengthening Eye Care Follow-Up Among Glaucoma Patients in Limpopo Province
Bibliographic record
Abstract
Background: Adherence to prescribed glaucoma medications is often poor and proper adherence can be challenging for most patients Objective: The purpose of this systematic literature review is to identify and evaluate studies that have tested the impact of each intervention on glaucoma adherence based on their quality outcome measure. Methods: A comprehensive search of database was conducted from January 2009 to January 2019. We systematically reviewed the literature and identified sixteen studies that used educational interventions to improve glaucoma medication adherence. Eleven out of 16 eligible studies were subjected to Randomized Controlled Trial (RCTs) and the remaining four were reviewed as observational studies. One study was reviewed through both observational plus randomized control trial method. Results: Out of the eleven (68.75%) RCTs interventions done, five (31.25%) showed improvement in medication adherence and persistence with eye drop instillation, whereas (n=6) did not show any significant improvement on their medication adherence. The quality of each study was evaluated using the Jadad score calculation and the Ottawa-Newcastle. Conclusion: Using information from this systematic review and Health Behavior Model, we created a theoretical framework to illustrate how counseling and education can improve medication adherence amongst glaucoma patients in the country.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".