Content and quality of websites supporting self-management of chronic breathlessness in advanced illness: a systematic review
Bibliographic record
Abstract
Chronic breathlessness is a common, burdensome and distressing symptom in many advanced chronic illnesses. Self-management strategies are essential to optimise treatment, daily functioning and emotional coping. People with chronic illness commonly search the internet for advice on self-management. A review was undertaken in June 2015 to describe the content and quality of online advice on breathlessness self-management, to highlight under-served areas and to identify any unsafe content. Google was searched from Sydney, Australia, using the five most common search terms for breathlessness identified by Google Trends. We also hand-searched the websites of national associations. Websites were included if they were freely available in English and provided practical advice on self-management. Website quality was assessed using the American Medical Association Benchmarks. Readability was assessed using the Flesch-Kincaid grades, with grade 8 considered the maximum acceptable for enabling access. Ninety-one web pages from 44 websites met the inclusion criteria, including 14 national association websites not returned by Google searches. Most websites were generated in the USA (n=28, 64%) and focused on breathing techniques (n=38, 86%) and chronic obstructive pulmonary disease (n=27, 61%). No websites were found to offer unsafe advice. Adherence to quality benchmarks ranged from 9% for disclosure to 77% for currency. Fifteen (54%) of 28 written websites required grade ⩾9 reading level. Future development should focus on advice and tools to support goal setting, problem solving and monitoring of breathlessness. National associations are encouraged to improve website visibility and comply with standards for quality and readability.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".