<p>Understanding Patient Perspectives on Medication Adherence in Asthma: A Targeted Review of Qualitative Studies</p>
Bibliographic record
Abstract
Abstract: Adherence to asthma medications is generally poor and undermines clinical outcomes. Poor adherence is characterized by underuse of inhaled corticosteroids (ICS), often accompanied by over-reliance on short-acting β 2 -agonists for symptom relief. To identify drivers of poor medication adherence, a targeted literature search was performed in MEDLINE and EMBASE for articles presenting qualitative data evaluating medication adherence in asthma patients (≥ 12 years old), published from January 1, 2012 to February 26, 2018. A thematic analysis of 21 relevant articles revealed several key themes driving poor medication adherence, including asthma-specific drivers and more general drivers common to chronic diseases. Due to the episodic nature of asthma, many patients felt that their daily life was not substantially impacted; consequently, many harbored doubts about the accuracy of their diagnosis or were in denial about the impact of the disease and, in turn, the need for long-term treatment. This was further compounded by poor patient-physician communication, which contributed to suboptimal knowledge about asthma medications, including lack of understanding of the distinction between maintenance and reliever inhalers, suboptimal inhaler technique, and concerns about ICS side effects. Other drivers of poor medication adherence included the high cost of asthma medication, general forgetfulness, and embarrassment over inhaler use in public. Overall, patients’ perceived lack of need for asthma medications and medication concerns, in part due to suboptimal knowledge and poor patient-physician communication, emerged as key drivers of poor medication adherence. Optimal asthma care and management should therefore target these barriers through effective patient- and physician-centered strategies. Keywords: Inhaled corticosteroids, over-reliance, patient-physician communication, respiratory tract disease, short-acting β 2 -agonist, underuse
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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 | 0.038 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".