Reducing sedentary behavior in individuals with COPD: healthcare professionals’ perspectives
Bibliographic record
Abstract
Reducing sedentary behavior (SB) in individuals with chronic obstructive pulmonary disease (COPD) is being increasingly recognized as a novel health target. Understanding healthcare professionals (HCPs) behavior that influences a reduction in SB in this population could facilitate achieving this target. To explore the determinants of behavior related to HCPs targeting a reduction in SB in people with COPD. We used a qualitative semi-structured interview approach informed by the Theoretical Domains Framework (TDF). Sixteen HCPs were interviewed. Interview transcripts were mapped against the relevant TDF domain(s) and then higher order themes were generated. Directed content analysis resulted in mapping 949 quotes to the TDF domains with environmental context and resources being the most coded domain. Three higher order themes were identified: 1) HCPs need more knowledge on reducing SB; 2) Strategies suggested to include in pulmonary rehabilitation (PR) to reduce SB; and 3) Barriers to adding SB to PR. Domains of environmental context and resources, knowledge, social/professional role and identity, reinforcement, social influences, skills and beliefs about capabilities were relevant to the study population to reduce SB in people with COPD. Knowledge of SB varied across participants. This study provided information on potential behavioral targets for future interventions that involve HCPs and aim to reduce SB among people with COPD.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".