Perspectives of healthcare professionals and patients on the application of mindfulness in individuals with chronic obstructive pulmonary disease
Bibliographic record
Abstract
OBJECTIVE: To explore the views of healthcare professionals (HCPs) and patients towards mindfulness for individuals with COPD. METHODS: A qualitative study design informed by and analyzed using deductive thematic analysis. Twenty HCPs, with at least one year's clinical experience in COPD management and 19 individuals with moderate to severe COPD participated in semi-structured interviews. RESULTS: Analysis revealed seven themes. 1. Mindfulness is difficult to articulate and separate from relaxation. 2. Mindfulness has a role in disease management. 3. Mindfulness therapy should be optional. 4. Preferred techniques include; breathing meditation, music and body scan. 5. Mindfulness should be delivered by knowledgeable, enthusiastic and compassionate trainers. 6. Preferred mode of delivery is shorter sessions delivered alongside pulmonary rehabilitation, with refresher courses 7. Efficacy should be assessed using psychological outcome measures and qualitative methodologies. CONCLUSIONS: Mindfulness appears to be an attractive therapy for individuals with COPD. An understanding of the perspectives of HCPs and patients should inform the delivery of such programs. PRACTICAL IMPLICATION: Individuals with COPD were comfortable using breathing to reduce anxiety. Stigma and negative preconceptions were considered barriers to participation. Short sessions delivered by experienced trainers were preferred. A combination of methodologies should be used to examine effectiveness.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".