Implementation of an Acute Care COPD Exacerbation Patient Mobilization Tool. A Mixed-Methods Study
Bibliographic record
Abstract
Improving the mobility of hospitalized patients with an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a priority of care. AECOPD-Mob is a clinical decision-making tool for physical therapists, especially those who are newly graduated or are new to caring for patients with AECOPDs in acute care settings. Although this tool has been available for several years, dissemination via publication is not sufficient to implement it in clinical practice. The primary objective of this study was to develop, implement, and evaluate different formats of AECOPD-Mob in an acute care setting. We used a mixed-methods, convergent parallel design. In addition to the paper format of AECOPD-Mob, we developed a smartphone app, a web-based learner module, and an in-service learning session. Newly graduated physical therapists (PTs) or PTs new to the practice area were recruited from urban acute care hospitals. Participants used the different formats for 3 weeks and then completed the Post-Study System Usability Questionnaire. User data were retrieved for the learning module. Participants participated in focus groups at 3 weeks and 3 months. Eighteen (72% of eligible PTs, 100% female, 94% graduated within 3 yr) PTs participated. Post-Study System Usability Questionnaire scores for the learning module and smartphone indicated that participants were satisfied with these formats (median score 2.0 on 1-7 Likert Scale for both technology formats, lower scores indicating greater satisfaction). However, the participants reported in the focus group that the paper format was preferred over other formats. Concerns with the smartphone app included infection control and the perception of lack of professionalism when using a smartphone during clinical practice. The learning module and in-service were considered helpful as an introduction but not as an ongoing support. The paper format was seen as the most efficient way to access the necessary information and to facilitate communication between other members of the care team about the importance of mobility for hospitalized patients with AECOPDs. Newly graduated PTs strongly preferred the paper format of the AECOPD-Mob tool in the acute care setting. Future research will focus on knowledge translation strategies for other health disciplines.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".