Association of Asthma Self-efficacy to Asthma Control and Quality of Life
Bibliographic record
Abstract
BACKGROUND: Achieving optimal asthma control relies upon several behavioral factors (self-monitoring, treatment adherence) that may be influenced by asthma self-efficacy (ASE). PURPOSE: To assess the extent to which levels of ASE are associated with asthma control and asthma-related quality of life in patients with asthma. METHODS: A total of 557 adult patients with documented asthma completed a battery of questionnaires (Asthma Self-Efficacy Scale, ASES; Asthma Control Questionnaire, ACQ; Asthma Quality of Life Questionnaire, AQLQ) and standard spirometry. Patients also underwent a sociodemographic, medical history, and psychiatric interview. Partial correlations adjusting for covariates were conducted to assess associations between ASES scores and ACQ and AQLQ scores. RESULTS: Results indicated that ASES scores were negatively correlated with ACQ total score and individual items scores, indicating that higher levels of ASE were associated with improved asthma control, and positively correlated with AQLQ total and subscale scores, indicating that higher levels of ASE were associated with better asthma-related quality of life. All findings were independent of covariates. CONCLUSIONS: Findings suggest that being confident in one's ability to control asthma symptoms is associated with better asthma control and quality of life. Future studies should assess the direction of the association between self-efficacy and asthma morbidity in order to determine optimal treatment targets.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".