Impact of a Behavioral-Based Intervention on Inspiratory Muscle Training Prescription by a Multidisciplinary Team
Bibliographic record
Abstract
INTRODUCTION: Our goal was to compare behavioral- and information-based interventions aimed at increasing prescription of inspiratory muscle training (IMT) for people with chronic obstructive pulmonary disease (COPD) by interdisciplinary teams during pulmonary rehabilitation (PR). METHODS: Six hospital PR programs were randomly assigned to a behavioral- or information-based intervention. Both interventions provided evidence supporting IMT and its prescription details. However, the behavioral-based intervention focused on barriers and challenges to IMT prescription informed by a nationwide survey and the theory of planned behavior (TPB). It included hands-on practice and content, in part, was driven by learners' questions. In contrast, the information-based intervention delivered information in a typical didactic education session followed by a demonstration and question period. It was supplemented with evidence-based research articles. The primary outcome was the change in prescription rate of IMT for COPD patients by determining the difference during the 6 months preceding compared to the 6 months during the interventions. RESULTS: Sixty-one health professionals and 488 COPD outpatients within 6 PR programs participated. No COPD patients were prescribed IMT at any of the sites during the 6-month preintervention phase. The behavioral-based intervention resulted in an IMT prescription rate of 10.2% to people with COPD, whereas the information-based intervention resulted in no IMT prescriptions. DISCUSSION: A behavioral-based intervention that is based on TPB and addresses challenges identified by health professionals is more effective than a traditional lecture approach to increase health professionals' prescription of IMT for patients with COPD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".