<p>Guided asthma self-management or patient self-adjustment? Using patients&rsquo; narratives to better understand adherence to asthma treatment</p>
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to better understand patients' perspective of asthma self-management by focusing on the sociocultural and medical context shaping patients' illness representations and individual decisions. PATIENTS AND METHODS: We conducted a secondary analysis of semi-structured interviews carried out as part of a multicentered collective qualitative case study. In total, 24 patients, aged 2-76 years with a confirmed diagnosis of asthma (or were parents of a child), who renewed the prescription for inhaled corticosteroids in the past year, participated in this study. The thematic analysis focused on asthma-related events and experiences reported by the patients. Consistent with narrative inquiry, similar patterns were grouped together, and three vignettes representing the different realities experienced by the patients were created. RESULTS: The comparison of experiences and events reported by the patients suggested that patients' perceptions and beliefs regarding asthma and treatment goals influenced their self-management-related behaviors. More specifically, the medical context in which the patients were followed (ie, frame in which the medical encounter takes place, medical recommendations provided) contributed to shape their understanding of the disease and the associated treatment goals. In turn, a patient's perception of the disease and the treatment goals influenced asthma self-management behaviors related to environmental control, lifestyle habits, and medication intake. CONCLUSION: Current medical recommendations regarding asthma self-management highlight the importance of the physicians' guidance through the provision of a detailed written action plan and asthma education. These data suggest that while physicians contribute to shaping patients' beliefs and perceptions about the disease and treatment goals, patients tend to listen to their own experience and manage the disease accordingly. Thus, a medical encounter between the patient and the physician, aiming at enhancing a meaningful conversation about the disease, may lead the patient to approach the disease in a more effective manner, which goes beyond taking preventative paths to avoid symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".