A Review of Factors Relating to Medication Non-Adherence in Patients with Schizophrenia
Bibliographic record
Abstract
Schizophrenia is a severe chronic mental illness affecting twenty million people worldwide. Although the incidence of schizophrenia remains low, its prevalence may remain high due to medication non-adherence. Knowing potential factors relating to non-adherence with medication among patients with schizophrenia by investigating existing literature is needed in order to understand the phenomenology of this situation. Therefore, the study aimed to explore the most common factors that affected medication non-adherence in patients with schizophrenia. A systematic review was conducted through a literature search on Cochrane Database of Systematic Reviews, MEDLINE, PsycINFO, EBSCOhost, CINAHL Complete, ERIC, and Allied Health databases published from 1980 to 2021. Database searches were conducted with the terms “non-adherence to medication” “schizophrenia,” and “factors.” Eighty-six articles were found following the first-round search. Then seventy-eight articles were excluded due to irrelevant and duplicate tiles. Only eight articles were included for the final review. Per the findings, factors associated with medication non-adherence were categorized into four main themes: individual characteristics, cognitive appraisal, social influence, and health-care service. To improve medication adherence rates in patients with schizophrenia, psychiatric nurses must consider these specific factors while establishing nursing interventions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".