Evaluation of online videos and websites on inspiratory muscle training for individuals with chronic lung disease
Bibliographic record
Abstract
BACKGROUND: Inspiratory muscle training (IMT) is an effective rehabilitation modality for individuals with chronic lung disease. IMT can improve dyspnea, exercise capacity, and health-related quality of life. Online resources are common sources of health information for individuals. This study is the first to: 1) evaluate the content, reliability, quality, and comprehensibility of IMT-related videos and websites for individuals with chronic lung disease, and 2) determine the characteristics of these online resources. METHODS: The search term "(respiratory muscle training) OR (inspiratory muscle training)" was used to evaluate the first 200 consecutive YouTube videos and 200 Google websites on IMT for chronic lung disease management. Online resources were evaluated using validated scoring metrics: modified DISCERN tool, Global Quality Scale (GQS), and Patient Education Materials Assessment Tools (PEMAT) understandability and actionability. Content comprising key IMT components was also scored. RESULTS: Forty videos and fourteen websites were included, with majority uploaded by for-profit organizations. Content scores (out of 25) were low (videos 7.7 ± 4.4; websites 11.4 ± 5.3, p = 0.01). Benefits of IMT were often highlighted, but safety considerations were infrequently mentioned. Resources scored poorly on modified DISCERN (videos 2/5 IQR[1.0-3.0]; websites 3.5/5 IQR[2.0-4.0], p = 0.001), and GQS scores (videos 2/5 IQR[2.0-3.0]; websites 3/5 IQR[2.8-3.3], p = 0.003). Online resources met the PEMAT threshold (>70 %) for understandability, but not actionability. CONCLUSIONS: Online IMT resources have mainly focused on the benefits of IMT and majority were developed by for-profit organizations. There is a need for high-quality, evidence-based online resources, as IMT is an important rehabilitation modality for chronic lung disease management.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".