Factors influencing medication adherence among patients with severe mental disorders from the perspective of mental health professionals
Bibliographic record
Abstract
BACKGROUND: Medication adherence is a common issue influenced by various factors among patients with severe mental disorders worldwide. However, most literature to date has been primarily quantitative and has focused on medication adherence issue from the perspective of patients or their caregivers. Moreover, research focused on medication adherence issue in China is scarce. Present study aims to explore the influential factors of medication adherence among patients with severe mental disorders form the perspective of mental health professionals in Hunan Province, China. METHODS: A qualitative study was performed in Hunan Province, China with 31 mental health professionals recruited from October to November 2017. And semi-structured interviews or focus group interviews were conducted along with audio recordings of all interviews. Interview transcripts were then coded and analyzed in Nvivo software with standard qualitative approaches. RESULTS: Three major themes influencing medication adherence among patients with severe mental disorders were identified as: (1) attitudes towards mental disorder/treatment; (2) inadequate aftercare; (3) resource shortages. CONCLUSIONS: This qualitative study identified the factors influencing medication adherence among patients with severe mental disorders in China. As a locally driven research study, it provides practical advice on medication adherence promotion for mental health workers and suggests culturally tailored models that improve the management of patients with severe mental disorders in order to reduce economic burden on individual and societal level.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".