Does treatment adherence correlates with health related quality of life? findings from a cross sectional study
Bibliographic record
Abstract
BACKGROUND: Although medication adherence and health-related quality of life (HRQoL) are two different outcome measures, it is believed that adherence to medication leads to an improvement in overall HRQoL. The study aimed to evaluate the association between medication adherence and HRQoL. METHODS: A questionnaire-based cross-sectional study design was undertaken with hypertension patients attending public hospitals in Quetta city, Pakistan. HRQoL was measured by Euroqol EQ-5D. Medication adherence was assessed by the Drug Attitude Inventory. Descriptive statistics was used to tabulate demographic and disease-related information. Spearmans correlation was used to assess the association between the study variables. All analysis was performed using SPSS 17.0. RESULTS: Among 385 study patients, the mean age (SD) was 39.02 (6.59), with 68.8% of males dominating the entire cohort. The mean (SD) duration of hypertension was 3.010.939years. Forty percent (n=154) had a bachelors degree level of education with 34.8% (n=134) working in the private sector. A negative and weak correlation (0.77) between medication adherence and EQ-5D was reported. In addition, a negative weak correlation (0.120) was observed among medication adherence and EQ-VAS. CONCLUSIONS: Correlations among the study variables were negligible and negative. Hence, there is no apparent relationship between the variables.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".