Opportunities to improve asthma and COPD prevention and care: insights from the patient journey obtained through focus groups
Bibliographic record
Abstract
BACKGROUND: The healthcare experiences of patients hold valuable insights for improving the quality of services related to their well-being. We therefore invited and explored the perspectives of patients living with asthma and chronic obstructive pulmonary disease (COPD) on their interaction with the systems supporting health, in order to identify opportunities to improve services to prevent, treat and manage these conditions. METHODS: Two virtual focus groups were held in August 2021, one for adult asthma and one for COPD, to learn of patients' experiences receiving care for these conditions in the Vancouver Coastal Health (VCH) region of British Columbia. Participants were recruited through online postings or their clinician. We discussed the care pathway for each condition and invited participants to share their experiences of the past 5 years, specifically their reflections on the process, including feelings, points of praise and frustration, and opportunities for improvement in this context. Composite patient journey maps were developed for each condition to reflect the experiences shared. Audio recordings of the focus groups were transcribed and used in qualitative data analysis. RESULTS: Thematic analysis revealed the following as possible areas for improvement: low public awareness of asthma and COPD and associated risk factors, non-standardised diagnosis pathways that delay diagnosis, and inconsistency in delivering valued aspects of care such as supports for self-management, trust-inspiring acute care, empowering patient communication and timely access to care. CONCLUSION: We successfully used focus groups to generate composite journey maps of the experiences of patients living with asthma (n=8) and COPD (n=9) to identify features that these patients consider important for improving the healthcare system for asthma and COPD in VCH. Health professionals, decision makers and patient advocates in VCH and beyond can consider these insights when evaluating, and planning changes to, current practices and policies in service delivery.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".