Photovoice exploration of physical activity norms and values among rural and remote pulmonary rehabilitation participants in British Columbia, Canada
Bibliographic record
Abstract
PURPOSE: Individuals with chronic obstructive pulmonary disease (COPD) engage in less physical activity compared to the general population, which can lead to worsened symptoms. In pulmonary rehabilitation (PR) programs, participants learn strategies to complete activities more easily. For such strategies to be effective, however, PR clinicians must understand their clients' activity values and practices within their geocultural contexts. In this qualitative study, our aim was to explore physical activity norms and values among people with COPD living in remote and rural locations, using Photovoice methodology. MATERIALS AND METHODS: We recruited 12 participants from rural PR sites in British Columbia, Canada. During two distinct seasons (winter and summer), participants photographed meaningful activities then completed semi-structured interviews. We analyzed transcripts using a three-step hermeneutic method, which revealed three themes. RESULTS: Participants discussed feeling conflicted regarding their COPD symptoms and physical activity, as difficulties in activity engagement cause stress, but remaining active also fosters a sense of purpose and well-being. Meanwhile, participants' activities are inextricably linked to their rural, remote, and seasonal environment. CONCLUSIONS: Our study provides insight into how people with COPD resiliently engage in activities in a rural environment with distinct weather variations. Findings highlight the importance of considering individual factors when recommending activities in PR programs.Implications for rehabilitationAlthough people with chronic lung disease often encounter difficulty and stress in completing their daily activities, they both recognize the importance of and derive great personal meaning from remaining active.The unique social, geographical, physical, and climatic environments of rural and remote dwelling people with chronic lung disease can both enable and challenge their activity engagement.Pulmonary rehabilitation (PR) programs and clinicians must situate their activity recommendations within the geographic contexts of their clients - which can vary across the seasons.Support for participants' mental health is a vital aspect of PR.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".