A psychosocial perspective of medication side effects, experiences, coping approaches and implications for adherence in hypertension management
Bibliographic record
Abstract
INTRODUCTION: This study examined whether psychosocial variables influenced patients' perception and experience of side effects of their medicines, how they coped with these experiences and the impact on medication adherence behaviour. METHODS: A hospital-based mixed methods study using quantitative and qualitative approaches was conducted with hypertensive patients. Participants were asked about side effects, medication adherence, common psychological symptoms and coping mechanisms with the aid of standard questionnaires and an interview guide. RESULTS: The experiences of side effects-such as palpitations, frequent urination, recurrent bouts of hunger, erectile dysfunction, dizziness, cough, physical exhaustion-were categorized as no/low (39.75 %), moderate (53.0 %) and high (7.25 %). Significant relationships between depression (x (2) = 24.21, p < 0.0001), anxiety (x (2) = 42.33, p < 0.0001), stress (x (2) = 39.73, p < 0.0001) and side effects were observed. A logistic regression model using the adjusted results for this association is reported-depression [OR = 1.9 (1.03-3.57), p = 0.04], anxiety [OR = 1.5 (1.22-1.77), p ≤ 0.001] and stress [OR = 1.3 (1.02-1.71), p = 0.04]. Side effects significantly increased the probability of individuals to be non-adherent [OR = 4.84 (95 % CI 1.07-1.85), p = 0.04] with social factors, media influences and attitudes of primary care givers further explaining this relationship. Personal adoption of medication modifying strategies, espousing the use of complementary and alternative treatments and interventions made by clinicians were the main forms of coping with side effects. DISCUSSION: Results from this study show that, in addition to a biomedical approach, the experience of side effects has biological, social and psychological interrelations. The results offer more support for the need for a multi-disciplinary approach to healthcare where all forms of expertise are incorporated into health provision and 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".