Evaluating patient adherence in heart disease
Bibliographic record
Abstract
Rheumatic heart disease (RHD) has essentially disappeared from the industrialized world, but remains the most common form of cardiovascular disease in people aged 25 and under in developing nations, with some 300,000 new cases being identified there every year. In Aswan, a rural part of southern Egypt, RHD affects about 2.3% of children, but although secondary prophylaxis treatments are effective in preventing progression of the disease, its efficacy is limited because of low patient adherence. A recent study evaluates secondary prophylaxis for RHD in this part of the country, and outlines the barriers to these treatments . Amira Balbaa of McMaster University in Ontario, Canada and her colleagues developed a 43-item questionnaire to identify the main barriers and facilitators of adherence to the prophylaxis, and used it to interview 29 patients aged between 5 and 15 years from the Aswan Heart Center. The questionnaire included three groups of questions, designed to evaluate capability, intention, and healthcare barriers. Of the 29 patients interviewed, nearly two thirds (65.5%) adhered to their prophylaxis treatment regime. This group showed a higher overall degree of capability and intention to do so, according to their responses. The results showed that two thirds of the adherent patients were knowledgeable about RHD, compared to just one fifth of the non-adherent patients. Likewise, nearly 80% of the adherent patients were aware of the consequences of missing doses of their prophylaxis, compared to just 40% of the non-adherent patients. By contrast, 90% of the non-adherent patients consciously chose to miss appointments at which they would receive treatment. The researchers say the gap in knowledge between adherent and non-adherent patients, combined with misconceptions linking prophylactic treatment with paralysis, are at the root of barrier to treatment. They acknowledge that their study is limited by the small and unrepresentative sample size, low statistical power, and self-reporting nature of their data collection method, but emphasize that their goal was to run a pilot study rather than draw any solid conclusions. Nevertheless, they believe that a general tool that systematically outlines the barriers to receiving prophylaxis is an important first step towards improving patients' adherence to the treatments. 1 “In my opinion, developing an education campaign that is specific for the target population is the best intervention,” says Balbaa, “and that is why it's so important to do research, and have continuous monitoring and evaluations of such programmes.”Other InformationPublished in: QScience.com Highlights, Published by Nature Research for Hamad bin Khalifa University Press (HBKU Press) License: http://creativecommons.org/licenses/by/4.0
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".