Exploring Provider and Patient Experiences with the PREM-C9+ in COPD Primary Care
Bibliographic record
Abstract
Patient experience plays a crucial role in chronic obstructive pulmonary disease (COPD) care, encompassing clinical, emotional, social, and relational factors. The PREM-C9, developed by Hodson et al. (2019) in the UK, is the first COPD-specific patient-reported experience measure. Our research explored the experiences of healthcare providers and patients with the adapted PREM-C9+ in Southwestern Ontario, Canada. Adaptations included updating provider titles to "primary care provider" and adding a 10th item to assess overall healthcare experiences, ensuring the tool’s relevance to Canada. Using a descriptive qualitative approach, focus groups with 14 healthcare providers and interviews with 4 patients were conducted. Reflexive thematic analysis identified challenges, including scoring, terminology, redundancy, and health and digital literacy barriers. Recommendations for improvement included simplifying language and clarifying instructions. The study emphasizes the importance of tools like the PREM-C9+ in patient-centered care and highlights the need for further research to improve their usability in diverse healthcare contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".