Impact of Adding a Decision Aid to Patient Education in Adults with Asthma: A Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Not providing adequate patient education interventions to asthma patients remains a major care gap. To help asthma patients and caregivers discuss inhaled controller medication use, our team has previously developed a decision aid (DA). We sought to assess whether adding this DA to education interventions improved knowledge, decisional conflict, and asthma control among adults with asthma. METHODS: A parallel clinical trial (NCT02516449). We recruited adults with asthma, aged 18 to 65 years, prescribed inhaled controller medication to optimize asthma control. Educators randomly allocated participants either to the education + DA or to the education group. At baseline and two-month follow-up, we measured asthma knowledge (primary outcome) with a validated self-administered questionnaire (score -37 to +37). Secondary outcomes included decisional conflict and asthma control. Blinded assessors collected data. Between the two time points, the within- and between-group changes were estimated by generalized linear mixed models. RESULTS: Fifty-one participants (response rate: 53%; age: 44 ± 13 years; women: n = 32) were randomized either to the education + DA group (n = 26) or to the education group (n = 25), and included in statistical analyses. Between baseline and follow-up, mean [95% CI] knowledge scores increased from 21.5 [19.9-23.2] to 25.1 [23.1-27.0] in the education + DA group (P = 0.0002) and from 24.0 [22.3-25.7] to 26.0 [24.0-28.0] in the education group (P = 0.0298). In both of the groups, decisional conflict and asthma control improved. There were no differences between groups. CONCLUSIONS: Education improved knowledge, decisional conflict, and asthma control whether the DA was added or not.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".