Low-Level Evidence Suggests that Perceived Ability to Evaluate and Trust Online Health Information is Associated with Low Health Literacy
Notice bibliographique
Résumé
Objective – To review, based on research evidence, the correlation between low health literacy and four outcomes of interest: (1) the ability to evaluate online health information based on (2) perceived reliability and accuracy, (3) trust in the Internet as an information source, and (4) the application of established evaluation criteria. Design – Systematic review and narrative synthesis. Setting –MEDLINE, PsycInfo, Web of Science, CINAHL, and Communication and Mass-media Complete as well as articles discovered through the snowball method. Subjects – 38 studies identified through a systematic literature search. Methods – An exhaustive list of potential articles was gathered through searching five online databases and Google Scholar, and hand searching of references. Inclusion and exclusion criteria were applied in a two-phase screening process in which two researchers participated to address reliability. Data, including study characteristics and metadata, predictors, assessment methods, and outcomes, were extracted from relevant studies, and then synthesized narratively. Main Results – Following duplication removal 13,632 records were retrieved, 254 of which were identified for full-text assessment. Thirty-eight studies met the eligibility criteria. All studies were non-experimental and therefore graded as a low level of evidence; 35 were cross-sectional designs, 1 a focus group, and 2 were observational studies. Studies varied widely in population definition and sample size and were published between 2001 and 2013, primarily in North America. Overall, a positive association was identified between health literacy and outcomes related to the ability to evaluate or trust Internet health information, while findings were inconsistent related to perceived quality of information and the application of evaluative criteria. Four studies examined the impact of health literacy levels on one or more of the outcomes of interest. The most prevalent outcome measure studied was trust in online health information, and the least prevalent was the use of evaluative criteria. The ability to trust online health information was assessed primarily through self-reporting, half of which utilized the eHealth Literacy scale, the majority of which indicated a positive correlation between education level or low health literacy and the perceived or actual ability to evaluate online health information. No studies on perceived information quality were found to utilize health literacy as an indicator. A positive association between educational level and trust in health information on the Internet was reported in ten studies, while two articles noted a similar correlation based on proxies for health literacy, including English language proficiency and comprehension comfort. In terms of the use of evaluation criteria, only one study focused on health literacy, indicating that those with low health literacy evaluate online health information based on search result placement, celebrity endorsement, image quality, and site authorship, and that they trust university researchers more than government or religious authorities to provide health information. No association was shown between readability or physician-provided online information and evaluation criteria while one study demonstrated that study participants with higher education tended to check author credentials more often when evaluating a website. Conclusion – Effective and informed evaluation of online health information is impacted by low health literacy.
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 | 0,031 | 0,239 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,008 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».