The burden of nonadherence among adults with asthma: a role for shared decision‐making
Bibliographic record
Abstract
A shared approach to decision-making framework has been suggested for chronic disease management especially where multiple treatment options exist. Shared decision-making (SDM) requires that both physician and patients are actively engaged in the decision-making process, including information exchange; expressing treatment preferences; as well as agreement over the final treatment decision. Although SDM appears well supported by patients, practitioners and policymakers alike, the current challenge is to determine how best to make SDM a reality in everyday clinical practice. Within the context of asthma, adherence rates are poor and are linked to outcomes such as reduced asthma control, increased symptoms, healthcare expenditures, and lower patient quality of life. It has been suggested that SDM can improve treatment adherence and that ignoring patients' personal goals and preferences may result in reduced rates of adherence. Furthermore, understanding predictors of poor treatment adherence is a necessary step toward developing effective strategies to improve the patient-reported and clinically important outcomes. Here, we describe why a shared approach to treatment decision-making for asthma has the potential to be an effective tool for improving adherence, with associated clinical and patient-related outcomes. In addition, we explore insights into the reasons why SDM has not been implemented into routine clinical practice.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".