Factors related to poor adherence in Latvian asthma patients
Bibliographic record
Abstract
BACKGROUND: The problem of nonadherence to therapy is a key reason of insufficient asthma control. Evaluating the beliefs about asthma medication, cognitive and emotional perceptions may help to identify patients with poor adherence to treatment in clinical practice which need additional attention in order to increase the likelihood of them taking their asthma medication according to the prescribed treatment protocol. The purpose of this study is to assess whether beliefs about asthma medication, cognitive and emotional factors are related to poor treatment adherence of asthma medication in a sample of asthma patients in Latvia. METHODS: Study subjects were asthma patients attending outpatient pulmonologist consultations in Latvia during September 2013 to December 2015. Beliefs about asthma medicine, cognitive and emotional factors related to asthma were determined in a cross-sectional, self-administered survey. The validated Beliefs about Medicines Questionnaire (BMQ) and the Brief Illness Perception Questionnaire (brief IPQ) were used. Treatment adherence was assessed using 5-item version of the Medication Adherence Reporting Scale (MARS). The total sample size was 352 patients. Logistic regression models were used to predict poor adherence to asthma treatment. The validity of each logistic regression model was assessed by the Hosmer/Lemeshow test. The main outcome measure was self-reported adherence to treatment. RESULTS: The more the patients agreed with the statement "My future health depends on my asthma medication" the lower the possibility of poor adherence to asthma treatment (OR 0.42; 95% CI 0.24-0.74). The more concerned the patients were in regard to long-term effects of their medication (OR 2; 95% CI 1.22-3.27), the higher the probability of poor treatment adherence. CONCLUSIONS: Screening asthma patients using the BMQ may help to identify those to benefit from interventions targeting their concerns and medication beliefs in order to improve adherence to asthma medication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".