Psychotropic medication non-adherence and its associated factors among patients with major psychiatric disorders: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Major psychiatric disorders are growing public health concern that attributed 14% of the global burden of diseases. The management of major psychiatric disorders is challenging mainly due to medication non-adherence. However, there is a paucity of summarized evidence on the prevalence of psychotropic medication non-adherence and associated factors. Therefore, we aimed to summarize existing primary studies' finding to determine the pooled prevalence and factors associated with psychotropic medication non-adherence. METHODS: A total of 4504 studies written in English until December 31, 2017, were searched from the main databases (n = 3125) (PubMed (MEDLINE), Embase, CINAHL, PsycINFO, and Web of Science) and other relevant sources (mainly from Google Scholar, n = 1379). Study selection, screening, and data extraction were carried out independently by two authors. Observational studies that had been conducted among adult patients (18 years and older) with major psychiatric disorders were eligible for the selection process. Critical appraisal of the included studies was carried out using the Newcastle Ottawa Scale. Systematic synthesis of the studies was carried out to summarize factors associated with psychotropic medication non-adherence. Meta-analysis was carried using Stata 14. Random effects model was used to compute the pooled prevalence, and sub-group analysis at 95% confidence interval. RESULTS: Forty-six studies were included in the systematic review. Of these, 35 studies (schizophrenia (n = 9), depressive (n = 16), and bipolar (n = 10) disorders) were included in the meta-analysis. Overall, 49% of major psychiatric disorder patients were non-adherent to their psychotropic medication. Of these, psychotropic medication non-adherence for schizophrenia, major depressive disorders, and bipolar disorders were 56%, 50%, and 44%, respectively. Individual patient's behaviors, lack of social support, clinical or treatment and illness-related, and health system factors influenced psychotropic medication non-adherence. CONCLUSION: Psychotropic medication non-adherence was high. It was influenced by various factors operating at different levels. Therefore, comprehensive intervention strategies should be designed to address factors associated with psychotropic medication non-adherence. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017067436.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".