Rapid Review of Medication Taking (Adherence) Among Patients With Rheumatic Diseases During the COVID‐19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: We aimed to identify, appraise, synthesize, and contextualize rapidly emerging reports on medication taking (adherence) among patients with rheumatic diseases during the COVID-19 pandemic. METHODS: We searched MEDLINE, EMBASE, and CINAHL for peer-reviewed communications, letters, and articles published during the COVID-19 pandemic evaluating medication taking among individuals with rheumatic diseases. We appraised assessment and reporting of medication adherence according to established definitions of 3 distinct problems of medication taking (i.e., noninitiation, poor implementation, and discontinuation) and pooled findings using random-effects models. RESULTS: We included 31 peer-reviewed studies in our synthesis from various jurisdictions, of which 25 described medication taking among rheumatology patients and 6 described medication prescribing among rheumatology providers. The pooled prevalence of overall medication nonadherence was 14.8% (95% confidence interval [95% CI] 12.3-17.2) and that of medication discontinuation (i.e., stopping of prescriptions) and poor implementation (i.e., not taking medication at the dose/frequency prescribed) as 9.5% (95% CI 5.1-14.0) and 9.6% (95% CI 6.2-13.0), respectively. Noninitiation (i.e., not starting/not filling new prescriptions) was not addressed. CONCLUSION: Medication taking among individuals with rheumatic diseases during the COVID-19 pandemic varies globally. Unclear reporting and extensive variation in research methods between studies create barriers to research replication, comparison, and generalization to specific patient populations. Future research in this area should use consistent and transparent approaches to defining and measuring medication taking problems to ensure that findings appropriately describe the epidemiology of medication adherence and have the potential to identify modifiable targets for improving patient care.
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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 | 0.045 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.035 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".