Assessment of online YouTube videos as a source of information and instruction for pulmonary rehabilitation
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Pulmonary rehabilitation (PR) benefits individuals with chronic respiratory conditions beyond COPD; however, the quality of online resources has not been evaluated. The aims of this study were to assess the content, quality, and comprehensibility of YouTube videos that provide PR to individuals with chronic lung diseases other than COPD. METHODS: A search was conducted on YouTube for videos related to PR on non-COPD conditions, with the first 350 videos screened for eligibility (2004-2024). Videos were assessed for content based on predefined scoring matrix derived from PR guidelines, evaluated for their quality using the modified DISCERN tool and Global Quality Scale (GQS), and assessed for their understandability and actionability using the Patient Education Materials and Assessment Tool. Engagement metrics including viewing rate and interaction index were also analyzed. RESULTS: Of the 59 videos included, there was significant heterogeneity in PR content (i.e. aerobic, strength training, flexibility, etc.). 83 % of the videos were published following the onset of COVID-19 pandemic (March 2020), and 85 % of the videos were not directed at specific disease states. Video quality was moderate, with median modified DISCERN and GQS of 3 IQR[3-4] and 3 IQR[2-4] out of 5, respectively. Mean understandability and actionability were above the 70 % threshold. Engagement metrics revealed that median video views were 2857 (IQR[637-10,729]), but engagement was low (1.4 % IQR[1.0-2.7]). CONCLUSION: The study highlights variability in PR content and moderate quality of videos, with reasonable comprehensibility. There is a need for more standardized and disease-specific PR online video resources for non-COPD states.
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".