A shared decision-making intervention between health care professionals and individuals undergoing Pulmonary Rehabilitation: An iterative development process with qualitative methods
Bibliographic record
Abstract
BACKGROUND: Pulmonary Rehabilitation (PR) services typically offer programmes to support individuals living with COPD make rehabilitation choices that best meet their needs, however, uptake remains low. Shared Decision-Making (SDM; e.g., Patient Decision Aids (PtDA)) interventions increase informed and values-based decision-making between individuals and healthcare professionals (HCPs). We aimed to develop an intervention to facilitate PR SDM which was acceptable to individuals living with COPD and PR HCPs. METHODS: An iterative development process involving qualitative methods was adopted. Broad overarching frameworks included: complex intervention development framework, the multiple stakeholder decision making support model, and the Ottawa Decision Support Framework. Development included: assembling a steering group, outlining the scope for the PtDA, collating data to inform the PtDA design, prototype development, alpha testing with individuals with COPD (n = 4) and PR HCPs (n = 8), PtDA finalisation, and design and development of supporting components. This took nine months. RESULTS: The PtDA was revised six times before providing an acceptable, comprehensible, and usable format for all stakeholders. Supporting components (decision coaching training and a consultation prompt) were necessary to upskill PR HCPs in SDM and implement the intervention into the PR pathway. CONCLUSIONS: We have developed a three-component SDM intervention (a PtDA, decision coaching training for PR healthcare professionals, and a consultation prompt) to support individuals living with COPD make informed and values-based decision about PR together with their PR healthcare professional. Clear implementation strategies are outlined which should support its integration into the PR pathway.
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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 | 0.128 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".