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Enregistrement W2969559585 · doi:10.2196/13240

Examining the Interaction Between Medical Information Seeking Online and Understanding: Exploratory Study

2019· article· en· W2969559585 sur OpenAlexvenueno aff
Rei Kobayashi, Masato Ishizaki

Notice bibliographique

RevueJMIR Cancer · 2019
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Literacy and Information Accessibility
Établissements canadiensnon disponible
Organismes subventionnairesJapan Society for the Promotion of Science
Mots-clésLikert scaleInformation seekingPsychologyExploratory researchThe InternetMedical educationInformation qualityInformation seeking behaviorScale (ratio)Quality (philosophy)Focus groupQualitative researchInformation literacyApplied psychologyMedicineInformation systemComputer scienceWorld Wide WebPedagogy

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Online information seeking on medical topics by patients can have beneficial effects by helping them decide on treatment options and fostering better relationships with doctors. The quality of websites and processes of seeking information online have mostly been studied, with a focus on the accuracy and reliability of websites; however, few studies have examined the relationship between other aspects of quality and the processes of seeking medical information online. OBJECTIVE: This exploratory study aimed to shed light on the quality of websites used for information seeking from the perspective of understanding medical information in combination with seeking it online. METHODS: The study participants were 15 Japanese university students with no problem using the internet. A questionnaire survey about health literacy (47 items on a 4-point Likert scale) and information navigation skills on the internet (8 items on a 5-point Likert scale) was conducted before participants engaged in online information seeking and qualitative interviews. The students searched for information on a disease and its treatment. The websites viewed were gathered from search behavior recorded by software and browser logs. Follow-up interviews were conducted to elicit explanations from the participants about the assignments and their views of online information seeking. The explanations were evaluated by 55 health care professionals on a 3-point Likert scale and then assessed based on their comments and the participant interviews. RESULTS: The mean age of the participants was 20.6 years (median 21; SD 1.06). All participants were able to access reliable websites with information relevant to the assignments. The mean ratings of the students' explanations were 108.6 (median 109; range=83-134) for the disease and 105.6 (median 104; range=87-117) for its treatment. The inter-rater reliability were 0.84 (95% CI 0.77-0.90) and 0.95 (95% CI 0.93-0.97), indicating good and excellent, respectively. The mean of the sum of the health literacy skills was 115.1 (median 115; range=80-166) and the mean for information navigation skills was 25.9 (median 26; range=17-36), respectively. Health literacy and information navigation skills were moderately correlated (r=0.54; 95% CI 0.033-0.822; P=.04). Among the four stages of health literacy, understanding and appraising (r=0.53; 95% CI 0.025-0.820; P=.04) were moderately correlated with information navigation skills (r=0.52; 95% CI 0.013-0.816; P=.046). The participants had no difficulties operating and browsing the internet and considered medical and public institution websites to be reliable; however, due to unfamiliarity with medical terms, they had difficulties choosing a site from the results obtained and comparing and synthesizing information provided by different sites. They also looked for sites providing orderly information in plain language but provided explanations from sites that gave inadequate interpretations of information. CONCLUSIONS: This study revealed interactions between searching the internet for, and understanding, medical information by analyzing the processes of information seeking online, physicians' evaluations and comments about the participants' explanations, and the participants' perceptions.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,286
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,226
Tête enseignante GPT0,501
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations13
Publié2019
Routes d'admission1
Résumé présentoui

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