Development of a Theory-Based Intervention to Increase Prescription of Inspiratory Muscle Training by Health Professionals in the Management of People with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is twofold: (1) to provide an overview of the literature on barriers to evidence-based practice (EBP) and the effectiveness of implementation interventions in health care; and (2) to outline the development of an implementation intervention for improving the prescription of inspiratory muscle training (IMT) by physical therapists and other health professionals for people with chronic obstructive pulmonary disease (COPD). SUMMARY OF KEY POINTS: Individuals, organizations, and the research itself present barriers to EBP in physical therapy. Despite the evidence supporting the use of IMT, this treatment continues to be under-used in managing COPD. Current health services research shows that traditional information-based approaches to implementation, such as didactic lectures, do not adequately address the challenges health professionals face when trying to make changes in practice. We propose the development of a theory-based intervention to improve health professionals' use of IMT in the management of COPD. It is postulated that a behavioural intervention, based on the theory of planned behaviour (TPB), may be more effective than an information-based strategy in increasing the prescription of IMT by health professionals. CONCLUSION: TPB may be used to understand the antecedents of health professionals' behaviour and to guide the development of implementation interventions. Further research is needed to evaluate the effectiveness of this proposed intervention in the management of people 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".