Low back pain websites do not meet the needs of consumers: A study of online resources at three time points
Bibliographic record
Abstract
Background: The popularity of the Internet as a source of health-related information for low back pain (LBP) is growing. Although research has evaluated information quality in health-related websites, few studies have considered whether content and presentation match consumer preferences. Objective: The aim of this study was to evaluate whether LBP website content and presentation matched preferences of consumers with LBP, whether matching preference of consumers changed over 8 years as recognition of people-centred healthcare has developed and whether this differs between countries of Internet searching. Method: The most prominent and top 20 LBP websites were identified using common search engines in 2010, 2015 and 2018. Websites identified in the top 20 in 2010 were followed up if not identified in 2015 and 2018. Two reviewers independently evaluated websites with a 16-item checklist developed from research of consumer preferences. In 2015, websites were identified using searches conducted using IP addresses from Australia, the United States of America (USA), the United Kingdom and Canada. After removal of duplicates, 55 websites were evaluated in 2010. In 2015 and 2018, 33 and 28 new sites, respectively, were identified, and 37 previous websites were re-evaluated. Results: In 2010 and 2015, websites predominantly originated from USA and were sponsored by “for-profit” organisations. In 2018, most websites originated from Australian “not-for-profit” organisations. None of the websites provided information on all content areas. At least 55% of websites were rated as poor or fair. No site rated as excellent overall. There was some worsening over time. Country of search did not affect results. Conclusion: Websites retrieved using typical searches did not meet information and presentation preferences of people with LBP.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".