Evaluating the Knowledge and Information-Seeking Behaviors of People Living With Multiple Sclerosis: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The internet has emerged as a primary source of health-related information for people living with multiple sclerosis (MS). However, given the abundance of misinformation found on the web, this behavior may pose a significant threat to internet users. OBJECTIVE: This study aims to explore the knowledge and information-seeking behavior of people living with MS followed at a specialized MS clinic where education is a cornerstone of care. METHODS: This cross-sectional survey-based study comprised 20 true or false statements, covering both scientific facts and popular misinformation about MS treatments. A "scientific fact score" and a "misinformation score" were calculated by attributing a scoring system to each point in the survey: +1 point was attributed to correct answers, -1 point was attributed to incorrect answers, and 0 point was attributed to "I don't know." Furthermore, the survey inquired about participants' health-seeking behaviors. RESULTS: The mean age of the 69 participants was 48.4 (SD 10.9) years, 78% (54/69) were female, 81% (56/69) were highly educated, 90% (62/69) were receiving a disease-modifying therapy, and 52% (30/58) had experimented with alternative therapies. The mean score for answering the scientific and misinformation questions correctly was 69% (SD 2.4%) and 22% (SD 4.5%), respectively (P<.001). Notably, when questioned about misinformation, answering correctly dropped significantly (P<.001), while indecision (P<.001) and answering incorrectly (P=.02) increased. Sociodemographic factors and medical questions were not significantly associated with scientific and misinformation scores (all P>.05); however, misinformation scores did significantly correlate with levels of education (P=.04). The main sources of health-related information were from expert-led MS websites (48/58, 82%) and health care professionals (34/58, 59%). Low-reliability sources were less used; however, word of mouth seemed to be prevalent (14/58, 24%), followed by Facebook (10/58, 17%). On average, people with MS reported having consulted 3 high- to moderate-quality sources and only 1 low-quality source. CONCLUSIONS: Education at the clinic and consulting primarily moderate- to high-quality sources did not safeguard against misinformation, indicating a need for more misinformation-geared education at the clinic. Notably, there is a need to proactively educate patients about misinformation commonly found on the web, and more importantly, create space for them to discuss the information without prejudice. As novel educational methods may be relatively more time-consuming, implementing change may be challenging. Furthermore, age, sex, education level, and health literacy might not safeguard against misinformation. Herein, we were unable to identify correlations associated with scores obtained on the questionnaire other than educational level. Although the educational level did seem to impact the misinformation score, this did not stop participants from experimenting with alternative therapies. Although studies are exploring novel ways to effectively deal with health misinformation on the web, more research is needed to fully understand this highly complex social phenomenon.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".