Web-information surrounding complementary and alternative medicine for low back pain: A cross-sectional survey and quality assessment
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is expected to globally affect up to 80% of individuals at some point during their lifetime. While conventional LBP therapies are effective, they may result in adverse side-effects. It is thus common for patients to seek information about complementary and alternative medicine (CAM) online to either supplement or even replace their conventional LBP care. The present study sought to assess the quality of web-based consumer health information available at the intersection of LBP and CAM. METHODS: We searched Google using six unique search terms across four English-speaking countries. Eligible websites contained consumer health information in the context of CAM for LBP. We used the DISCERN instrument, which consists of a standardized scoring system with a Likert scale from one to five across 16 questions, to conduct a quality assessment of websites. RESULTS: Across 480 websites identified, 32 were deemed eligible and assessed using the DISCERN instrument. The mean overall rating across all websites 3.47 (SD = 0.70); Summed DISCERN scores across all websites ranged from 25.5-68.0, with a mean of 53.25 (SD = 10.41); the mean overall rating across all websites 3.47 (SD = 0.70). Most websites reported the benefits of numerous CAM treatment options and provided relevant information for the target audience clearly, but did not adequately report the risks or adverse side-effects adequately. CONCLUSION: Despite some high-quality resources identified, our findings highlight the varying quality of consumer health information available online at the intersection of LBP and CAM. Healthcare providers should be involved in the guidance of patients' online information-seeking.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".