SELF-EFFICACY AND BELIEFS ABOUT MEDICATIONS: IMPLICATIONS FOR ANTIRETROVIRAL THERAPY ADHERENCE
Bibliographic record
Abstract
The earlier optimism generated by the efficacy of antiretroviral drugs in human immuno-deficiency virus (HIV) patients has been dissipated in the face of the enormous chal-lenge of maintaining a nearly perfect adherence indefinitely. This study set to determine the influence of HIV adherence self-efficacy and beliefs about medicines on antiretrovi-ral therapy adherence, with the aim of developing a framework for enhancing antiretrovi-ral therapy (ART) adherence through focused intervention on modifiable factors from study variables that are strongly associated with ART adherence. \nA descriptive correlational design was used to assess the predictive relationships of HIV adherence Self-Efficacy, Beliefs about Medicines and ART adherence among 232 HIV-infected patients in a large public health facility in Pretoria. Participants' medication be-liefs were assessed using the Beliefs about Medicines Questionnaire, HIV adherence self-efficacy was assessed with HIV adherence self-efficacy scale (HIV-ASES) and ART adherence was assessed using the AIDS Clinical Trial Group questionnaire. Pearson correlation analysis was used to assess bivariate associations among the variables, and multiple regression analysis was used to examine the relationships among the inde-pendent variables and ART adherence. \nMean adherence for the 232 participants was 95% (SD=13.2). Correlation analysis re-vealed positive bivariate associations between perceived general harm and overuse of medications, and ART adherence (p<0.05); between specific necessity and concerns about ARVs, and perceived general harm and overuse of medications (p<0.05); be-tween HIV adherence self efficacy and ART non-adherence (p<0.05). Multiple regres-sion analysis showed significance for perceived general harm and overuse of medica-tions on ART adherence (F(1;231)=11,583;p<0,001) with perceived general harmful ef-fects and overuse of medications explaining 4.8% of the variance. There was signifi-cance for HIV adherence self-efficacy on ART non-adherence (F(1;41)=4.440; p<0.041), with HIV-ASES explaining 9,8% of the variance. Based on the results, a framework for enhancing ART adherence was developed. Activities in the framework consist of baseline screening for adherence facilitators and barriers using the beliefs about medicine questionnaire and HIV ASES, this is followed by focused interventions on identified barriers of ART adherence
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".