Factors affecting medication adherence among patients with rheumatic disorders
Bibliographic record
Abstract
Background and objective: Patient's adherence is an important factor affecting the successful maintenance of treatment, slow progression of the disease; reduce costs of health care especially in the presence of multiple chronic conditions as rheumatic disorders. While, medication non-adherence is a significant problem leads to increased mortality and morbidity. So, identification of the factors affecting non-adherence to medication regimens is beneficial for healthcare providers to improve patient’s health condition. The aim of the study was to determine factors affecting medication adherence among sample of Egyptian patients with rheumatic disorders.Methods: Design: An exploratory descriptive research design. Subjects: Purposive sampling of patients with history of rheumatic disorders. Setting: The study was carried out in rheumatology department and medical wards at Al-Kaser Al-Aini hospital. Tool: Patient Preliminary Informational Variables, Morisky Medication Adherence Scale 8-Items and Factors affecting drug adherence checklist were used to collect pertinent data.Results: The study showed 59.2% of study group had low adherence, followed by medium adherence and high adherence (28%, 12.7%) to prescribed medications respectively. Findings also; revealed that the highest percent of these factors that may combine to render patients to be less able to adhere to prescribed medication ranked as complexity of medication regimen; chronic conditions, restricted formularies, changing medications covered on formularies; fear of possible adverse effects, fear of dependence; lack of continuity of care, treatment interferes with lifestyle or requires significant behavioral changes; patient information materials written at too high literacy level; severity of symptoms; lack of knowledge on adherence and the effective interventions for improving it; as well the medication cost; long wait times; burdensome schedule; poor access or missed appointments; actual or perceived unpleasant side effects; duration of therapy; medication negative effect on liver and kidney; in addition, psychosocial stress, anxiety and anger.Conclusions: Due to the diversity of causes of non-adherence, the health care professionals must understand factors affecting medication adherence when dealing with problems of medication adherence especially with chronic conditions as rheumatic disorders. Recommendation: Interventions for overcoming factors affecting adherence must become a central component of efforts to improve patients’ health worldwide. This could be done by proper determination for factors affecting medication adherence, also to consider patient condition individually and modify the treatment approach accordingly.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".