Evaluating the Knowledge and Information-Seeking Behaviors of People Living With Multiple Sclerosis: Cross-Sectional Questionnaire Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».